United States       Air and Radiation      EPA420-R-99-031
          Environmental Protection              December 1999
          Agency
4vEPA    Technical Support
          Document for the
          Tier 2/Gasoline Sulfur
          Ozone Modeling
          Analyses
                              > Printed on Recycled Paper

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                                           EPA420-R-99-031
                                              December 1999
                                    for the
Emissions Analysis and Monitoring Division
Office of Air Quality Planning and Standards
   U.S. Environmental Protection Agency

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Table of Contents

I     Introduction	      1

II     Ozone Modeling over the Eastern U.S	     1
      A.     Episode Selection	     1
             1.  Episodic Meteorological Conditions and Ozone Levels	   2
             2.  General Representativeness of Episodic Ozone as Compared to
                Design Values	     5
      B.     Domain and Grid Configuration	     6
      C.     Meteorological Modeling	     8
      D.     Development of Other UAM-V InputFiles	    9
      E.     Model Performance Evaluation	    9
             1.      Statistical Definitions 	     10
             2.      Evaluation Results	     11

in    Ozone Modeling over the Western U.S	     12
      A.     Episode Selection	     12
             1.  Episodic Meteorological Conditions and Ozone Levels	   12
             2.  General Representativeness of Episodic Ozone as Compared to
                Design Values	     13
      B.     Domain and Grid Configuration	    14
      C.     Meteorological Modeling	     16
      D.     Development of Other UAM-V Input Files  	    16
      E.     Model Performance Evaluation	    17

IV    Results of the Tier 2/Gasoline Sulfur Modeling for Ozone 	    18
      A.     Analysis of Need	     18
      B.     Impacts of Tier 2/Sulfur Program on Ozone Levels 	    18
             1.  Methods for Quantifying Impacts 	    19
                    a. Definition of Areas for Analysis	   19
                    b. Description of Ozone Metrics	    19
             2.  Impacts on Ozone in 2007 and 2030	     20
                    a. Impacts in 2007	     20
                    b. Impacts in 2030	     21
      C.     Additional Analyses to Support Responses to Comments	   21
             1.  Local-scale Model Performance	    21
             2.  Determination of Alternate Attainment Targets	    23
             3.  Estimation of Attainment/Nonattainment using Relative
                Reduction Factors 	     24

V     References	      26

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I. Introduction

       This document describes the ozone modeling performed as part of the Tier 2/Sulfur final
rulemaking.  The ozone modeling was conducted to support several components of the
rulemaking including (a) the determination of need for the Tier 2/Sulfur program, (b) the
benefits/cost analysis, (c) an assessment of the expected impacts of the program on future ozone
concentrations, and (d) the preparation of responses to comments on the proposed rulemaking.
The modeling involved simulations of the Urban Airshed Model-Variable Grid (UAM-V), (SAI
1996) in a regional mode for two modeling domains (eastern U.S. and western U.S.) which
together cover nearly all of the 48 contiguous States. Model runs were made for a 1996 Base
Year and four future-year emissions scenarios: a 2007 Base Case, a 2007 Tier 2/Sulfur Control
Case, a 2030 Base Case and a 2030 Control Case.  These scenarios, along with the procedures
followed to develop the emissions inventories for modeling, and the impacts of the control
scenarios on  emissions are described elsewhere (Pechan, 1999). For the eastern U.S., model
simulations were made for all five emissions scenarios.  However, since modeling for the West
was intended mainly to support the  benefit/cost analysis which was performed for 2030, only the
1996 base year and 2030 base case and control scenarios were modeled for the West.  As
described below, for both the East and West, emissions scenarios were modeled using
meteorological conditions for several multi-day  episodes when ambient measurements recorded
high ozone concentrations.  The remainder of this report includes a description of the modeling
system, the modeling episodes, the base year model performance over the eastern and western
U.S., and a discussion of the results of these simulations.
II.  Ozone Modeling over the Eastern United States

A.  Episode Selection

       There are several considerations involved in selecting episodes for an ozone modeling
analysis (EPA, 1999). In general, the goal should be to model several differing sets of
meteorological conditions leading to ambient ozone levels similar to an area's design value1.
Ideally, the modeling time periods would be supported by large amounts of ambient data to be
used in input development and model evaluation. The issue, in terms of regional modeling, is
how to meet these episode selection goals over a large number of individual ozone non-
attainment areas without having to model several entire ozone seasons (impossibly time
consuming and resource-intensive). It is inevitable that the chosen episodes will feature
observed ozone lower than the design value in some  areas and greater than the design value in
other areas. For the Tier 2/Sulfur analyses, we focused on the summer of 1995 for selecting the
       1 Typically defined as the fourth-highest 1-hour daily maximum ozone observed over a
three year period at a specific monitor.

                                           1

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episodes to model in the East because 1995 is a recent time period for which we had model-ready
meteorological inputs and the summer of 1995 contained one of the four episodes used by the
Ozone Transport Assessment Group (OTAG) for modeling regional ozone over the eastern U.S.

       Based on a review of observed daily maximum ozone concentrations across the eastern
U.S. during June through August, three episodes were selected for ozone modeling: June 12-24,
July 5-15, and August 10-21. The start of each episode was picked to correspond to days with no
ozone exceedances.  Thirty episode days were modeled in all, not including the three ramp-up
days used in each episode to minimize the effects of initial conditions. The meteorological
conditions and ozone levels during each episode are described below.

       1.     Episodic Meteorological Conditions  and Ozone Levels

       Warm temperatures, light winds, cloud-free skies,  and stable boundary layers are some of
the typical characteristics of ozone episodes.  On a synoptic scale, these conditions usually result
from a combination  of high pressure aloft (500 millibars) and at the surface. At a smaller scale,
the conditions that lead to local ozone exceedances can vary from location to location (based on
factors such as wind direction, sea/lake breezes, etc.)  The meteorological and resultant ozone
patterns for the three Tier 2 modeling  episodes are discussed in  more detail below.

       June 12-24.  1995

       The initial stages of this episode were fairly typical from the standpoint of regional
meteorology. A 500-millibar ridge propagated into the eastern U.S. from the west.  The ridge
was associated with  a surface high that migrated south from Canada.  A cold front passed
completely through the region by June 13 (Wednesday) allowing the modeling to start with a
clean set of initial conditions. Maximum temperatures during the June 15 - 17-period were
generally in the 80s  and little precipitation was measured.  By June 17, a strong (1028 mb)
surface high was anchored over the  region.

       The observed ozone fields in the early part of the episode were high (e.g., 125-130 ppb)
only in locations such as Houston, Beaumont, and Lake Michigan. It was not until June 17 that
concentrations exceeded 100 ppb over large parts of the domain (i.e., Midwest and Northeast
Corridor).

       However, as the aloft pattern amplified, a cut off low developed over the southeastern
U.S. On the 19th and 20th, cooler temperatures and occasional rain prevailed in the Southeast.
This resulted in a temperature pattern that featured maximums of 90-100 degrees F  over the
northern tier of States and 75-85 degrees F in the south. Additionally, the strong cyclonic
circulation around this low resulted in aloft flow from east to west over the mid-Atlantic and
Ohio Valley States.  Ozone continued to build throughout this period in the Northeast, peaking
on the  19th and 20th with values greater than 125 ppb common from Washington, D.C. to Boston.

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       The last four days of the episode were relatively clean in the Northeast due to the
combination of a "backdoor" cold front and the northward migration of the cut off low.
Meanwhile ozone conducive conditions returned to the Texas Gulf Coast and Lake Michigan
areas. The highest value over the entire summer of 1995 (210 ppb) was recorded near Houston
on the 22nd. The episode came to an end on the 25th as a long-wave trough replaced the 500-mb
ridge over the eastern U.S.

       Table II-1 shows a State-by-State listing of daily exceedance counts during the June 1995
Tier 2 episode. There were 85 exceedances of the ozone NAAQS during this period. The peak
day of the episode was June 19.  Texas had the most exceedances (28).

       Table II-l.  Summary of exceedance  days, by State/day, for the June 1995 Tier 2 episode.

6/12/95
6/13/95
6/14/95
6/15/95
6/16/95
6/17/95
6/18/95
6/19/95
6/20/95
6/21/95
6/22/95
6/23/95
6/24/95
AL













AR













CT





1
2
3
2




DE







3
2




DC






1
1





FL













GA













IL












4
IN



1


1





2
KY













LA








2


2

ME













MD






1
7
1




MA







2





MI





4







M












2
NH













NJ







4
3




NY







2





NC













OH







1





OK













PA







8





RI







1





SC













TN













TX

1
1
1
1



3
7
7
4
3
VA






1






W













WI




1
2







TOT
0
1
1
2
2
7
6
32
13
7
7
6
11
       July 5-15. 1995

       The mid-July episode, which covered most of the Ozone Transport Assessment Group
(OTAG) July 1995 episode, is much easier to characterize from a meteorological perspective. A
strong 500-mb ridge progressed from west to east across the eastern U.S. over the period.  This
feature was centered over Colorado on the 8th, over Kansas on the 11th, over Illinois on the 13th,
and over Pennsylvania on the 15th.  The ridge finally flattened out on the 16th allowing a surface
cold front to clean out the northern portions of the domain and less stable conditions to prevail
over the southern portions.

       Excessively hot temperatures accompanied the core of this strong ridge.  Temperatures in
the 90s and 100s were common throughout the episode. Rainfall was confined primarily to the
coastal regions in the south and southeast. Wind speeds were moderate and the mean transport
direction was southwest to northeast,  especially over the northern half of the domain.

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       From the 8th through the 10th, the airmass over the eastern U.S. was gradually becoming
hazy.  Ozone hot spots occurred in urban areas like Houston, Dallas, and Atlanta. By the 11th, the
area of regional haze (roughly defined as the area where peak ozone was greater than 75 ppb) had
expanded to encompass most of the domain.  On top of that "background," local contributions
from urban emissions yielded ozone exceedances in places like Kansas City, St. Louis,
Birmingham, Dallas, Memphis, Atlanta, Baton Rouge, Evansville, Louisville, Cincinnati,
Chicago, Milwaukee, Columbus, and Baltimore/Washington on the 11th and 12th.

       July 13 and 14 marked the highest regional ozone levels of the summer as most sites,
with the exception of those in the Southeast, exceeded 100 ppb.  Almost all major metropolitan
areas in the northern two-thirds of the domain measured values greater than 125 ppb on this day.
For the 14th and 15th, most of the ozone problem shifted east and south due to both transport and
the location of the aloft core of warm air.  The Northeast Corridor, Charlotte, Greensboro,
Birmingham, and Atlanta all had exceedances of the standard on this day. The episode ended
abruptly on the 16th (Sunday) for most of the domain, although elevated ozone lingered over the
southern regions into the early part of the next week.

       Table II-2 shows a State-by-State listing of daily exceedance counts during the July 1995
Tier 2 episode.  There were 199 exceedances of the  ozone NAAQS during this period.  The peak
day of the episode, in terms of exceedance monitors was July 14. Texas had the most
exceedances (26).

Table II-2. Summary of exceedance days, by State/day, for the July 1995 Tier 2 episode.

7/05/95
7/06/95
7/07/95
7/08/95
7/09/95
7/10/95
7/11/95
7/12/95
7/13/95
7/14/95
7/15/95
AL






1
1


1
AR






1

1


CT








5
7
3
DE









3
3
DC











FL











GA




1
4
3




IL








8
2
2
IN







1
1
2
2
KY







1



LA






1
1



ME









1

MD







5
2
4
10
MA








3
2

MI








7
6
1
M





1
3
4
6


NH











NJ







1

7
6
NY








1
5
3
NC









3

OH







3
4
3

OK











PA








1
3
5
RI









3

SC











TN






1
1

1

TX


2
2
4
1
5
5
6
1

VA










4
W








1


WI







7



TOT
0
0
2
2
5
6
15
30
46
53
40
       August 7-21. 1995

       A one-day ozone event occurred over New England on August 10, and a separate one-day
event occurred in the Lake Michigan region on the 12th. By the 14th, high pressure aloft and at
the surface dominated the eastern half of the U.S. Temperatures ranged from 90 to 100 degrees F
over most of the domain throughout this period.  Ozone was highest over Georgia, Tennessee,

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Kentucky, North Carolina, and Virginia during this period. Hurricane Felix brushed the East
Coast from the 16th - 18th, but appeared to have little effect on ozone or ozone transport away
from the immediate eastern seaboard.

       A weak cold front, draped across the Great Lakes over most of the episode, moved slowly
southward over the eastern half of the Appalachians during the August 18-21 period. This front
initiated precipitation that helped keep ozone concentrations low in the upper Midwest.  The 18th
featured high ozone across the South in cities such as: Atlanta, Charlotte, Birmingham, Augusta,
as well as St. Louis. On the 19th and 20th, as the front slid further south, ozone air quality
improved over this region as well.  Only sites in Texas and Louisiana remain above 125 ppb.
The 21st marked the fourth day that the same airmass has resided over the Northeast and it had
become fairly polluted by that point.

       Table II-3 shows a State-by-State listing of daily exceedance2 counts during the August
1995 Tier 2 episode.  There were 90 exceedances of the ozone NAAQS during this period.  The
peak day of the episode, in terms of exceedance monitors was August 21st. Texas had the most
exceedances (15).

Table II-3.  Summary of exceedance days, by State/day, for the August 1995 Tier 2 episode.

8/07/95
8/08/95
8/09/95
8/10/95
8/11/95
8/12/95
8/13/95
8/14/95
8/15/95
8/16/95
8/17/95
8/18/95
8/19/95
8/20/95
8/21/95
AL



1
1
1

1
1


4



AR















CT














3
DE














3
DC















FL















GA




1



3
2
2
5
1


IL





4





1



IN





1


1






KY









3
2




LA





1






2

1
ME



1











MD







3






3
MA



2










2
MI





1
1








M











1



NH



1











NJ







1






2
NY














2
NC







1


1




OH















OK















PA








1





1
RI














1
SC











1



TN










1




TX




1





1

6
6
1
VA







1
2






W















WI















TOT
0
0
0
5
3
8
1
7
8
5
7
12
9
6
19
       2.  General Representativeness of Episodic Ozone as Compared to Design Values

       In order to examine the representativeness of ozone levels during the episodes selected
for modeling, a comparison was made between the daily maximum observed values to recent
       2 An exceedance is a daily maximum 1-hour ozone concentration >=125 ppb.

                                           5

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design values.  In this analysis, the magnitude of county-specific design values for 1996-1998
were compared to the highest through 5th highest concentrations measured in the county during
the three episodes. Counties with design values (DV) >120 ppb were selected for analysis in
order to focus on concentrations approaching and exceeding the NAAQS. As can be seen in
Table II-4, 64 percent of the 110 counties examined have design values within 15 ppb of the
highest observed ozone in the  Tier 2 episodes.  Additionally, the second-high observed value
yields more values below the design value than above it.  The results indicate that the selected
episodes contain measured ozone concentrations that are generally representative of recent design
values over a large portion of the eastern U.S.

Table II-4.  Summary of Comparing the Five Highest Daily Maxima to Recent Design Values.
Ranking of
Observation
within Tier 2
Days
Highest ozone
2nd high ozone
3rd high ozone
4th high ozone
5th high ozone
# of cases in which the
observed was greater
than the design value by
15 ppb
32
10
2
0
0
# of cases in which the
observed was within 15
ppb of the design value
70
80
71
57
45
# of cases in which the
observed was less than
the design value by 15
ppb
8
20
37
53
65
B.  Domain and Grid Configuration

       As with episode selection, there are also several considerations involved in selecting the
domain and grid configuration to be used in the ozone modeling analysis. The modeling domain
should encompass the area of intended analysis with an additional buffer of grid cells to
minimize the effects of uncertain boundary condition inputs.  Grid resolution should be
equivalent to the resolution of the primary model inputs (emissions, winds, etc.) and equivalent
to the scale of the air quality issue being addressed. For the eastern U.S., the regional/national
Tier 2 analyses used the previously established OTAG domain to model regional ozone. The
western U.S. domain is discussed in Section HI.

       The Tier 2 UAM-V modeling was completed using two grids of varying extent (shown in
Figure II-1) and resolution as described below.

Main Grid:   Resolution: 1/2 longitude, 1/3 latitude (approximately 36 km)
             East-West extent: -99 W to -67 W
             North-South extent: 26 N to 47 N
             Vertical extent: Surface to 4 km
             Dimensions: 64 by 63 by 9

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Nested Grid3: Resolution:  1/6 longitude, 1/9 latitude (approximately 12 km)
             East-West extent: -92 W to -69.5 W
             North-South extent: 32 N to 44 N
             Vertical extent: Surface to 4 km
             Dimensions: 137 by 110 by 9
       The vertical layers were consistent between the two grids: 0-50, 50-100, 100-300, 300-
600, 600-1000, 1000-1500, 1500-2000, 2000-2500, 2500-4000.  All model heights are in meters
above ground level.  The number of vertical layers is greater than past regional-scale modeling
applications (e.g., OTAG) and was intended to better capture the depth of the planetary boundary
layer.

       This modeling domain allows for the consideration of future residual ozone exceedances
and the effects of Tier 2 emissions reductions over most major metropolitan areas in the eastern
U.S. (The Dallas-Fort Worth area may be the  exception given its proximity to the western
boundary.)
Figure II-l. Map of the Tier 2 Eastern modeling domain. The outer box denotes the entire
modeling domain (36 km) and the inner box indicates the fine grid location (12 km).
       3 Model concentrations are not calculated for the outer periphery of the nested grid. Two
buffer rows and columns are needed to solve the advection portion of the mass balance equation.

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C.  Meteorological Modeling

       In order to solve for the change in pollutant concentrations over time and space, the air
quality model requires certain meteorological inputs that, in part, govern the formation, transport,
and destruction of pollutant material. In particular, the UAM-V model used in the Tier 2
analyses requires five meteorological input files: wind (u- and v-vector wind components),
temperature, water vapor mixing ratio, atmospheric air pressure, and vertical diffusion
coefficient. Fine grid values of wind and vertical diffusivity are used; the other fine grid
meteorological inputs are interpolated from the coarse grid files.

       The gridded meteorological data for the three historical 1995 episodes were developed by
the New York Department of Environment and Conservation (NYDEC) using the Regional
Atmospheric Modeling System (RAMS), version 3b.  RAMS (Pielke et. a/., 1992) is a numerical
meteorological model that  solves the full set  of physical and thermodynamic equations which
govern atmospheric motions.  The output data from RAMS, which is run in a polar stereographic
projection and a sigma-p coordinate system, are then mapped to the UAM-V grid. Two separate
meteorological UAM-V inputs, cloud fractions and rainfall  rates, were developed based on
observed data.

       RAMS was run in a nested-grid mode with three levels of resolution: 108 km, 36 km, and
12 km with 2S-344 vertical layers. The top of the surface layer was 16.7 m in the 36 and 12km
grids. The two finer grids were at least as large as their UAM-V counterparts. In order to keep
the model results in line with reality, the simulated fields were nudged to an European Center for
Medium-Range Weather Forecasting (ECMWF) analysis field every six hours.  This assimilation
data set was bolstered by every four-hourly special soundings regularly collected as part of the
North American Research  Strategy on Tropospheric Ozone (NARSTO) field study in the
northeast U.S.

       A summary of the settings and assorted input files employed in this RAMS application
are listed below in Table D-5.  For more detail on the meteorological model configuration, see
Lagouvardos et al. (1997).

       A limited model performance evaluation (Sistla, 1999) was completed for a portion of the
1995 meteorological modeling (July 12-15).  Observed data not used in the assimilation
procedure were compared against modeled data at the surface and aloft. In general, there were no
widespread biases in temperatures and winds. Furthermore, the meteorological fields were
compared before and after  being processed into UAM-V inputs. It was concluded that this
preprocessing did not distort the meteorological fields.
       4 34 layers were used in the inner nested grids. 28 layers were modeled in the outer 108
km grid.

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Table II-5. Summary of RAMS model settings and inputs.
Model Setting/Input File
Input- Topography
Input - Sea-surface temperature
Input - Vegetation type
Input - Initial conditions
Input - Soil moisture
Setting
Setting - Lateral boundary conditions
Setting - Horizontal diffusivity
Setting - Vertical diffusivity
Setting - Shortwave/Longwave radiation
Description
30 arc -second data from EROS Data Center.
Mean monthly climatological data from NCAR.
10 arc-minute data from NOAA/NGDC.
The model was initialized with gridded one-degree ECMWF
data sets prepared by the isentropic analysis package.
Surface observations provided by SUNY were blended into
the initialization fields.
Six layer soil model. Assumed deeper layers were more
moist than near-surface layers.
Non-hydrostatic
Klemp-Wilhelmson
Smagorinsky
Mellor and Yamada parameterization scheme
Mahrer and Pielke
D.  Development of Other UAM-V Input Files

       The manmade emissions inventories for the five modeling scenarios were processed
through EMS-95 (Alpine Geophysics, 1994) in order to develop the UAM-V-ready, day-specific
emissions. Biogenic emissions were developed using the BEIS-2 model (Birth and Geron,
1995).  In addition, the photochemical grid model requires several other types of data. In general,
most of these miscellaneous model files were taken from existing regional modeling
applications.  Clean conditions were used to initialize the model and as lateral and top boundary
conditions as in OTAG (OTAG, 1997).

       The model requires information regarding land use type and surface albedo for all Layer 1
grid cells in the domain. Existing OTAG data were used for these non-day-specific files.
Photolysis rates were developed using the JCALC portion of the UAM-V modeling system.
Turbidity values were set equal to a constant thought to be representative of regional conditions.

E.  Model Performance Evaluation
       The goal of the base year modeling was to reproduce the atmospheric processes resulting
in high ozone concentrations over the eastern United States during the three 1995 episodes

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selected for modeling. Note that the base year of the emissions was 1996 while the episodes are
in 1995.  The effects on model performance of using 1996 base year emissions for the 1995
episodes are expected to be small.

       An  operational model performance evaluation for surface ozone for the 1995 episodes
was performed in order to estimate the ability of the modeling system to replicate base year
ozone concentrations. This evaluation is comprised principally of statistical assessments of
model versus observed pairs. The robustness of an operational evaluation is directly proportional
to the amount and quality of the ambient data available for comparison.

       1.  Statistical Definitions

       Below are the definitions of statistics used for the evaluation.  The format of all the
statistics is such that negative values indicate model ozone predictions that were less than their
observed counterparts.  Positive statistics indicate model overestimation of surface ozone.
Statistics were not generated for the first three days of an episode to avoid the initialization
period.  The operational statistics were generated on a regional basis in accordance with the
primary purpose of the modeling which is to assess the need for, and impacts of, a national
mobile source emissions control program. The statistics were calculated for (a) the entire Tier 2
domain and (b) four quadrants (Midwest, Northeast,  Southeast, Southwest).  The statistics
calculated for each of these areas are:

Domainwide unpaired peak prediction accuracy: This metric simply compares the peak
concentration modeled anywhere in the selected area against the peak ambient concentration
anywhere in the same area.  The difference of the peaks (model - observed) is then normalized by
the peak observed concentration.

Peak prediction accuracy: This metric averages the paired peak prediction accuracy calculated for
each monitor in the subregion.  It characterizes the capacity of the model to replicate peak
(afternoon) ozone over a subregion.  The daily peak model versus daily peak observed residuals
are paired in space but not in time.

Mean normalized bias: This performance statistic averages the normalized (by observation)
difference (model - observed) over all pairs in which the observed values were greater than  60
ppb. A value of zero would indicate that the model over predictions and model under predictions
exactly cancel each other out.

Mean normalized gross error: The last metric used to assess the performance  of the Tier 2/Sulfur
base cases is  similar to the above statistic, except in this case it is the absolute value of the
residual which is normalized by the observation, and then averaged over all sites. A zero gross
error value would indicate that all model concentrations (in which their observed counterpart was
greater than 60 ppb) exactly matched the ambient values.
                                            10

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       2.  Evaluation Results
       As with previous regional photochemical modeling studies, the Tier 2 base year
simulations are accurate representations of the historical ozone patterns at certain times and
locations and poor representations at other times and locations over this large modeling domain.
From a qualitative standpoint, there appears to be considerable similarity on most  days between
the observed and simulated ozone patterns. Additionally, where possible to discern, the model
appears to follow the day-to-day variations in synoptic-scale ozone fairly closely.  Other relevant
observations, in terms of model performance, are listed below.

      Mean normalized bias and mean normalized gross error values are similar  to OTAG
       performance statistics for the entire domain and the four quadrants as summarized in
       Table II-6.

Table II-6. Tier 2 Base Year model performance for the entire grid and by quadrant.
Mean Normalized
Bias
Domain
Midwest
Northeast
Southeast
Southwest
OTAG
1988
-8
-15
-3
+2
-6
OTAG
1991
-4
-8
-6
+15
+6
OTAG
1993
+1
-8
-8
+21
+2
OTAG
1995
+4
-5
+8
+9
+12
Tier 2
June 95
-10
-11
-17
-4
+2
Tier 2
July 95
-6 (-4)5
-13 (-8)
-9 (-9)
+4 (+5)
+8 (+&}
Tier 2
August 95
+2
+7
-9
+7
+6
Mean Normalized
Gross Error
Domain
Midwest
Northeast
Southeast
Southwest
OTAG
1988
28
27
29
28
22
OTAG
1991
25
26
23
25
24
OTAG
1993
27
25
23
32
23
OTAG
1995
25
24
26
27
29
Tier 2
June 95
24
24
27
20
24
Tier 2
July 95
24 (24)
26 (25)
22 (21)
24 (24)
27 (26)
Tier 2
August 95
23
22
24
22
24
       5 Values in parentheses are for the 10-15th only.  These dates correspond with OTAG
1995 episode days.

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       In general, the model under predicts ozone for the June and July episodes (-10 and -6
       percent, respectively).  This underestimation bias generally occurs over the first half of an
       episode. The latter portions of these episodes are generally unbiased.  Model
       performance is best over the southern portions of the domain.

       Mean normalized gross error ranges from 22 to 23 percent. Bias and errors are generally
       lowest in the Southwest region (fewest number of observed sites).

       The model typically underestimates the peaks as well as the mean ozone, but not as
       severely.

       Although the overall tendency (June/July episodes) is to underestimate the observed
       ozone, there are several instances in which large overestimations occurred.

       The model is slightly biased toward overestimation in the August episode (2.1 percent).
       Only the Northeast quadrant is underestimated (-9.1 percent) in this episode.

       While there are no established statistical  criteria for evaluating the adequacy of regional
       modeling applications, the relatively low values of bias and error plus the OTAG-
       equivalent performance indicate the modeling is sufficient for a national assessment of
       the need for (and impact of) Tier 2/Sulfur controls.
III.  Ozone Modeling over the Western United States

A.  Episode Selection

       For the western modeling, there were no existing meteorological data sets suitable for
regional ozone modeling. Measured ozone concentrations were examined for several recent
years to find representative ozone episodes over the western U.S. As with the East, the ambient
data were analyzed in order to identify time periods which captured episodic conditions in as
many areas as possible.  As a result of this analysis, two episodes from the summer of 1996 were
selected for the western U.S. modeling: July 5-15 and July 18-31.  An additional advantage
associated with the selection of 1996 episodes is the meteorological inputs and emissions inputs
are both from the same year.  Nineteen episode days were modeled in all, not including the three
ramp-up days used in both  episodes to minimize the effects of initial conditions.

       1.  Episodic Meteorological Conditions and Ozone Levels

       July 5-15. 1996

       July 6 marked the beginning of the development of a large 500 millibar ridge over the
western U.S.  A thermal low was located over the California desert and average summertime

                                           12

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conditions (light winds, warm temperatures, and little to no precipitation) existed over much of
the region.  Ozone was high (i.e., greater than 125 ppb) in the Sacramento, San Joaquin Valley,
and Los Angeles basins over the first five days of the episode. Most of the rest of the region did
not experience elevated amounts of ozone, with the exception of the Salt Lake City region on
July 7 and 8 (as high as 117 ppb).

       Over the period from July 11 to July 14, the ridge strengthened along the Pacific Coast
displacing the jet stream north into Canada. Wind speeds aloft were quite low during this period
along the West Coast resulting in little pollution advection or dispersion. Observed ozone values
greater than 100 ppb were monitored in urban areas all along the Pacific Coast (Redding =110
ppb, Eugene =117 ppb, Portland = 145 ppb, Seattle/Tacoma =118 ppb). Elevated levels of
ozone continued throughout this period in the Sacramento, the San Joaquin Valley, and Los
Angeles airsheds.

       Further east, the highest ambient ozone of the summer in Albuquerque (111 ppb) and El
Paso (112 ppb) were recorded on July 11. Vigorous northwest flow aloft which was associated
with a trough over the central U.S. prevented ozone buildup in Denver (until July 15 when a 107
ppb value was recorded).

       Monsoonal rains kept ambient ozone relatively low  in Arizona cities such as Phoenix and
Tucson. The episode ended (even in Los  Angeles and other California cities) when a strong 500
millibar trough progressed through the region from July 15-17.  This period of reduced ozone
conveniently allowed the modeling for the second July 1996 episode to be initialized with clean
conditions (ozone and ozone precursors).

       July 18-31. 1996

       Like the early July 1996 episode, this scenario began with a developing ridge over the
western U.S. on July 21. Ozone was confined to the major  cities of California until the July 23-
28 period, by which time the anticyclone aloft had strengthened and dominated the Pacific States
and the Desert Southwest. Temperatures  rose into the 100s in Oregon and Washington.  No
precipitation was recorded anywhere west of the Rocky Mountains; cloud cover was limited as
well over this region.

       As a result, ozone levels rose as well in areas such as Portland (149 ppb on 7/26),
Phoenix (127 ppb on 7/23),  Seattle (112 ppb on 7/26) and Salt Lake City (110 ppb on 7/26). The
ridge flattened out somewhat by July 29, but ozone values remained high in California and the
Arizona cities (Phoenix) through the end of the episode.

       2. General Representativeness of Episodic Ozone to Design Values

       Ozone in the western U.S. tends to be much less regionally pervasive than in the eastern
U.S. In general, ozone non-attainment conditions are more local in nature. This makes it more
                                           13

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difficult to capture in a few episodes all of the conditions that lead to individual design-value
levels of ozone in western areas. Table ni-1 compares 1996-1998 ambient design values against
the highest observed ozone levels in the Tier 2 episodes for the major non-California
metropolitan areas in the West6.  The results indicate that the peak ozone levels during the
episodes modeled are generally in the range of the design values. For example, only in Portland,
OR is the measured peak not within 15 ppb of the recent design value.

Table III-l. Comparison of 1996-1998 design values for major metropolitan areas in the
western U.S. against the highest observed ozone value in those same areas recorded during the
periods July 8-15, 1996 and July 21-31, 1996.
Area
Albuquerque
Denver
El Paso
Phoenix
Portland
Salt Lake City
Seattle
1996- 1998 Design Value
97
112
123
120
133
123
121
Highest ozone recorded in the Tier
2 episode days
111
107
112
127
149
117
118
B.     Domain and Grid Configuration

       The Tier 2 UAM-V modeling for the western U.S. was completed using a domain
containing two nested grids of varying extent and resolution as described below and shown in
Figure IJJ-1. The modeling used a latitude-longitude coordinate system as indicated below.
Main Grid:
Resolution: 1/2 longitude, 1/3 latitude (approximately 36 km)
East-West extent: -127 W to -99 W
North-South extent: 26 N to 52 N
Vertical extent: Surface to 4800 meters
Dimensions: 56 by 78 by 11
       6 Capturing representative ozone levels in California is less important in this analysis
because the equivalent of the Tier 2/Sulfur standards have been adopted there.
                                           14

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Nested Grid7:
Resolution: 1/6 longitude, 1/9 latitude (approximately 12 km)
East-West extent: -125 W to -103 W
North-South extent: 31 N to 49 N
Vertical extent: Surface to 4800 meters
Dimensions: 132 by 162 by 11
Figure III-l. Map of the Tier 2 modeling domain. The outer box denotes the entire modeling
domain (36 km) and the inner box indicates the fine grid location (12 km).

The vertical layers were consistent between the two grids: 0-50, 50-100, 100-300, 300-600, 600-
1000, 1000-1500, 1500-2000, 2000-2500, 2500-3100, 3100-3800, 3800-4800. All model heights
are in meters above ground level.
       7 Model concentrations are not calculated for the outer periphery of the nested grid. Two
buffer rows and columns are needed to solve the advection portion of the mass balance equation.

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C.     Meteorological Modeling

       The gridded meteorological data for the two historical 1996 episodes were developed
using the Fifth-Generation NCAR / Penn State Mesoscale Model (MM5). MM5 (Grell et. al.,
1995) is a numerical meteorological model that solves the full set of physical and thermodynamic
equations which govern atmospheric motions.

       MM5 was run in a nested-grid mode with three levels of resolution: 108 km, 36km, and
12 km with 23 vertical layers.  The model was simulated in five day segments with an eight hour
ramp-up period.  The MM5 runs were started at OZ, which is 4PM PST.  The first eight hours of
each five day period were removed before being input into UAM-V. The UAM-V runs start at
midnight, and each day runs from midnight to midnight (PST).

       MM5 is a terrain-following sigma-pressure coordinate model and was run using a
Lambert conformal map projection, therefore the data were processed to match the UAM-V grid
structure. There was also an issue in that several of the UAM-V grid boundaries extended
slightly beyond their counterpart MM5 12 km and 36 km domain boundaries (mostly over the
Pacific Ocean).  In these cases, data from the next outer grid were mapped to these areas. A
preprocessor (MM52UAMV) generates model-ready UAM-V files for wind, temperature, water
vapor, pressure, and vertical diffusion from the MM5 output. For more information on the
preparation of non-emissions-related inputs, see SAI (1999).

       The standard version of MM5 was revised for this project to output the internally-
calculated vertical diffusivities (Kv) generated as part of the Medium Range Forecast (MRF)
model boundary layer scheme. When the MRF boundary layer option is employed these  Kv
values represent non-local vertical exchanges.  This approach should provide the most
representative mixing field; one that captures both large- and small-scale vertical diffusive
fluxes.

       Unlike the eastern UAM-V modeling, the cloud fraction and rainfall rate inputs were
derived from the meteorological model as opposed to interpolating observed data to the model
grid. This alternative procedure was used because of the relatively sparse meteorological
observation network in the western U.S. Cloud fractions were diagnosed from the MM5 results
based on the assignment of a critical relative humidity, which if exceeded, indicated the presence
of a cloud.  The fractional extent of the cloud was a function of the amount the model humidity
exceeds the threshold value. Rainfall rates are extracted directly from MM5.
D.     Development of Other UAM-V Input Files

       The manmade emissions for the three scenarios were processed through the Sparse
Matrix Operator Kernal Emissions (SMOKE) modeling system (Houyoux and Vukovich, 1999)
for stationary sources and EMS-95 for mobile sources in order to develop the model-ready, day-

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specific emissions. Biogenic emissions were developed using BEIS-2. The initial, lateral
boundary, and top boundary species concentrations were set to clean values intended to represent
background-like concentrations. All species were set to values prescribed in EPA (1991), except
CO (200 ppb), VOC (25 ppb)8,  ozone (30 ppb), NO (0 ppb), and NO2 (1 ppb). Model land use
characteristics (as percentages of specified 11 categories) were derived from a 200 meter
resolution U.S. Geological Survey data base. Albedo values (needed in the calculation of
photolysis rates) were taken from this same data base. The aerosol optical depth which governs
the amount of UV scattering due to airborne particulates was set to a nominal value (0.094)
indicative of rural conditions. The photolysis rate lookup table required for the UAM-V runs
was developed,  as in the eastern U.S. simulations, via the JCALC preprocessor program.
E.     Model Performance Evaluation
      An operational evaluation was performed for the western modeling using the same
procedures and statistics discussed in section II-E.  Model performance measures were calculated
over the entire modeling domain, the 12 km fine grid, and 10 individual areas (Albuquerque,
Denver, El Paso, Phoenix, Portland, Salt Lake City, the San Joaquin Valley, Seattle, San
Francisco, and Southern California).  Table ni-2 contains the operational evaluation statistics.

Table III-2. Model performance statistics for individual areas in the western U.S.
Region
Albuquerque
Denver
El Paso
Phoenix
Portland
Salt Lake City
San Joaquin Valley
Seattle
San Francisco
Southern California
Unpaired Peak
Prediction
Accuracy
-0.287
-0.190
-0.356
-0.292
-0.041
-0.225
-0.309
0.017
-0.404
-0.436
Average Peak
Prediction
Accuracy
-0.313
-0.287
-0.400
-0.326
-0.180
-0.308
-0.390
-0.200
-0.375
-0.505
Mean
Normalized Bias
-0.331
-0.318
-0.443
-0.361
-0.227
-0.343
-0.404
-0.211
-0.395
-0.524
Mean Normalized
Gross Error
0.331
0.319
0.443
0.362
0.263
0.348
0.406
0.284
0.396
0.530
       Model performance for the 1996 base year episodes was characterized by underestimates
        Divided across CB-FV VOC species as specified in EPA guidance.

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of observed ozone as summarized in Table in-3. The model underestimated mean ozone by
about 30 ppb on average. On an individual area basis, the model did somewhat better in the
northwest portion of the domain (Portland and Seattle) where negative biases were about 15-25
percent. In the end, the modeling was determined to be sufficient for the intended purpose, that
is, to be used in a relative sense to assess the impacts of the Tier 2/Sulfur controls as part of the
economic benefits calculations.

Table III-3.  Summary model performance statistics for surface ozone in the western U.S. fine
grid.
(Units in percent)
Peak prediction accuracy
Mean normalized bias
Mean normalized gross error
Episode 1
(July 5 -15, 1996)
-39.0
-40.6
40.8
Episode 2
(July 2 1-31, 1996)
-37.5
-38.6
39.1
IV.  Results of the Tier 2/Gasoline Sulfur Modeling for Ozone

       The results of the Tier-2 modeling were further analyzed to provide information to (a)
support the determination of the need for Tier 2, (b) examine the air quality impacts of the
program, and (c) support the preparation of responses to comments on the proposed rulemaking.
The analyses for each of these purposes are described below.

A.  Analysis of Need

       To support the determination of the need for Tier 2, the modeling results were examined
to identify those CMSA and MSAs that have predicted exceedances of the 1-hour NAAQS in the
2007 and/or in the 2030 baseline scenarios.  Model predicted exceedances are defined as daily 1-
hour maximum concentrations >=125 ppb. A CMSA/MSA is determined to contain an
exceedance if at least one of the model grid cells assigned to the area has at least one exceedance
during the episodes modeled.  The procedures for assigning grid cells to areas are defined below.
The CMS A/MS As with predicted 2007 base case and/or 2030 base case exceedances are listed in
Appendix IV-1.

B.  Impacts of the Tier 2/Sulfur Program on Ozone Levels

       The forecasted impacts on ozone concentrations as a result of the Tier 2 program were
analyzed by comparing model predictions from the 2007 and 2030 Tier 2 control cases to those
in the corresponding 2007 and 2030 base case scenarios.  For 2007, the impacts of Tier 2 are
derived from model simulations for the three episodes in June, July, and August 1995. For 2030,
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model runs were made for the June and July 1995 episodes only. Thus, the 2030 impacts are
based on predictions from these two episodes.  As indicated above, the first 3 days of each
episode are considered as initialization or "ramp up" days and are, therefore, excluded from the
analysis of results.

       The focus of the analysis is on ozone levels in the eastern U.S. since this region contains
most of the areas with 1-hour nonattainment.  In particular, the impacts were quantified for
CMSA/MSAs with predicted future-year exceedances of the 1-hour ozone NAAQS. As
indicated above, predicted exceedances are modeled 1-hour daily maximum concentrations
>=125 ppb.  The methods for determining which areas have forecasted exceedances are described
below. For the eastern U.S. modeling there were 48 such areas in 2007 while for 2030 there
were 38 areas with base case exceedances9. Note that the 2030 scenario had a different number
of areas for analysis compared to 2007 in part because some areas only had exceedances
predicted in the August episode which was not modeled for 2030 (i.e., Charleston, Cincinnati,
Huntington, Indianapolis, Lakeland, Macon, Melbourne, Norfolk, Orlando, Pensacola, and
Wheeling) and because one area (i.e., York) had predicted exceedances in the 2030 base case but
not the 2007 base case.

       1.  Methods for Quantifying Impacts

       a.  Definition of Areas for Analysis

       In order to analyze the impacts of the Tier 2/Sulfur emissions reductions, it was necessary
to "link" or assign the model's grid cells to individual CMSA/MSAs. The rules for assigning
grid cells to CMSA/MSAs (i.e., areas) is as follows. The first step was to assign grid cells to
States based on the fraction of the grid cells' area in a State. A grid cell was assigned to the  State
which contains most of the cells' area. Next, grid cells were assigned to an individual
CMSA/MSAs if (1) the grid is wholly contained within the CMSA/MSA or (2) partially within
(i.e., overlapping) the area, but not also partially within another CMSA/MSA.  Grid cells that
partially overlap two or more CMSA/MSAs are assigned to the county, and thereby the
corresponding CMSA/MSA, which contains the largest portion of the grid cell. Each grid cell in
the "coarse" or 36 km grid portion of the domain was divided into nine 12 km grids before
applying the preceding methodology.  The number of grid cells assigned to each area is listed in
Appendix IV-2.

       b.  Description of Ozone Metrics

       The impacts of Tier 2 on ozone were quantified using a number of metrics (i.e., measures
of ozone concentrations). These metrics include:
       9 These areas are listed in Appendix IV-1.  Portland, OR is on the list of areas with
predicted future exceedances, but was not included in this analysis which focused only on areas
in the eastern U.S.

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       (1) the peak 1-hour ozone concentrations,
       (2) the number of exceedances,
       (3) the total amount of ozone >= 125 ppb,
       (4) the decrease in ozone, on average, and
       (5) the increase in ozone, on average.

(1) The peak 1-hour ozone represents the highest ozone prediction within the area (i.e., CMSA or
MSA) across all episodes modeled.

(2) The number of exceedances is the total number of grid cells with predicted exceedances in
the area across all days. This exceedance metric counts each grid cell every day there is a
predicted exceedance in that grid. Thus, an individual grid cell can be counted more than once if
there are multiple days with predicted exceedances in that grid.

(3) The total amount of ozone above 125 ppb in an area is determined by taking the difference
between the predicted daily maximum ozone concentration and 125 ppb (i.e., daily maximum -
125 ppb) in each grid cell and then summing this amount across all grid cells in the area and days
modeled. This metric is referred to as "the amount of nonattainment".

(4) The decrease, on average is determined by first summing all the reductions predicted in those
grid cells with daily maximum ozone >=125 ppb in the base case (i.e., base case exceedances).
This total reduction is then divided by the number of base case exceedances in the area to yield
the "ppb" decrease that occurs, on average, for the exceedances predicted in the area.

(5) The increase, on average is determined by summing any increases in ozone that occur in
values already >= 125 ppb in the base case together with any increases that cause a value below
125 ppb in the base case to go above 125 ppb in the control case.  This total increase is then
divided by the number of exceedances in the base case.

       The impacts of Tier 2 on ozone were examined for the individual CMSA/MSAs as well
as by aggregating the metrics across all areas (i.e., 48 in 2007 and 38 in 2030) to obtain the
overall impact expected from the program. The values of the metrics are provided in Appendix
IV-3 for 2007 and Appendix IV-4 for 2030.

       2. Impacts on Ozone in 2007 and 2030

       a. Impacts in 2007

       The ozone modeling results for 2007 show that Tier 2 will  provide nearly a 10%
reduction in the total number of exceedances predicted across all 48 CMSA/MSAs combined.
Overall, the total amount of nonattainment is predicted to decline by about 15%. Looking at the
results for individual areas, over half (i.e., 31 of the 48) areas have fewer exceedances with Tier 2
in 2007. In five of these  31 areas, the exceedances are eliminated by Tier 2 (i.e., Macon,
                                          20

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Melbourne, Norfolk, Pittsburgh, and Rochester). Of the 48 areas, 14 are expected to have no
change in the total number of exceedances.  Only 3 areas (i.e., Chicago, Detroit, and New
London) are predicted to have an increase in exceedances.  In each of these areas the increase
occurs in only one grid cell on one day, which is a small impact considering the total number of
grid cells in these area.

       In the vast majority of areas (i.e., 45 out of 48), the decrease in ozone, on average is
greater than any predicted increase, on average. In fact, only two areas (i.e., Chicago and Detroit)
are predicted to have an increase, on average of more than  a half ppb (6 other areas had an
increase, on average of between one tenth and a half ppb).  However, even these two areas are
predicted to have a reduction in the peak ozone concentration.

       b. Impacts in 2030

       In the 2030 base case the number of exceedances increases relative to the amount of 125
ppb or greater cells in the 2007 base case in most areas, when looking at a consistent set of
episode days. This reflects the increase in emissions as growth outpaces the effects of controls.
Meanwhile, the overall number of exceedances is reduced  32% due to the Tier 2/Sulfur controls
in 2030 while the amount of nonattainment is predicted to  decline by 36%. Of the 38 areas,
nearly all (i.e., 32) are predicted to have a decrease in the number of exceedances while the
remaining six areas are predicted to have no change.  None of the areas are predicted to have an
increase in exceedances. However, in two of the 38 areas (i.e., Chicago and Detroit) the amount
of ozone nonattainment is predicted to be larger in the control case than in the 2030 base case.
C.  Additional Analyses to Support Responses to Comments

       Several additional analyses were performed to support the responses to comments on the
proposed rule. These analyses include (a) an evaluation of the Tier 2 regional model
performance analogous to EPA's urban ("local") scale model performance recommendations
(EPA, 1991), (b) a determination of "alternative attainment targets" for individual areas
considering the episodes modeled, and (c) an estimation of attainment/nonattainment based on
relative reduction factors.

       1.  "Local" Scale Model Performance

       Several comments were received on the Tier 2 notice of proposed rulemaking to the
effect that model over predictions could be leading to overestimates of residual non-attainment
areas. To support the response to this comment, a local-scale evaluation was conducted on the
final modeling to ensure that the determination of Tier 2/Sulfur need was not significantly biased
                                          21

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due to model performance.  Statistics were calculated for 36 "local" subregions10 using the
procedures described in Section II.  Generally speaking, model performance will typically appear
poorer for individual local subregions than when the performance statistics are averaged over
larger regions. This results due to the heightened sensitivity of local-scale model results to local
input uncertainties.  Table IV-1 contains the area-wide unpaired peak prediction accuracy, the
average peak prediction accuracy, mean normalized bias and mean normalized gross error values
for the 36 local-scale subdomains averaged over the 30 episode days.  Some conclusions
regarding model performance at the local scale are listed below.

Table IV-1. Tier 2 Base Case model performance for 36 local subregions.
Region
Dallas
Houston-Galveston
Beaumont-Port Arthur
Baton Rouge
New Orleans
St. Louis
Memphis
Birmingham
Atlanta
Nashville
Knoxville
Charlotte
Greensboro
Raleigh-Durham
Evansville-Owensboro
Indianapolis
Louisville
Cincinnati-Dayton
Columbus
Huntington-Ashland
Chicago
Milwaukee
Muskegon-Grand Rapids
Gary-South Bend
Detroit
Pittsburgh
Central PA
Norfolk
Richmond
Baltimore-Washington
Delaware
Philadelphia
Unpaired Peak
Prediction Accuracy
-0.140
-0.098
-0.023
0.070
0.285
-0.008
0.133
0.140
0.151
0.163
-0.031
0.153
0.099
0.039
0.170
0.011
0.143
0.005
0.005
0.167
0.217
0.333
0.124
0.022
0.095
0.010
0.094
0.149
0.203
0.022
0.080
-0.013
Average Peak
Prediction Accuracy
-0.102
0.059
0.103
0.223
0.143
-0.037
-0.128
0.073
0.014
0.050
-0.217
0.010
-0.015
-0.090
0.004
-0.113
0.068
-0.053
-0.109
0.123
-0.227
-0.187
-0.142
-0.236
-0.190
-0.101
-0.083
-0.078
0.007
-0.067
-0.071
-0.166
Mean Normalized
Bias
0.192
0.248
0.202
0.289
0.222
0.193
0.215
0.171
0.225
0.262
0.271
0.173
0.174
0.176
0.230
0.206
0.271
0.225
0.196
0.235
0.288
0.234
0.221
0.286
0.270
0.228
0.218
0.221
0.183
0.199
0.150
0.251
Mean Normalized Gross
Error
-0.085
0.006
0.052
0.178
0.141
-0.029
-0.108
0.034
-0.008
0.096
-0.188
0.001
-0.007
-0.098
0.037
-0.056
0.096
-0.034
-0.095
0.070
-0.058
-0.026
-0.090
-0.128
-0.115
-0.056
-0.048
-0.064
-0.013
-0.068
-0.072
-0.135
       10 For evaluation purposes, these local areas were defined by simple boxes of grid cells
around a given area. They do not correspond to non-attainment areas or CMS A/MS As.
                                           22

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New York City
Hartford
Boston
Maine
0.130
0.054
0.186
0.189
-0.190
-0.125
-0.147
-0.147
0.281
0.220
0.238
0.224
-0.120
-0.102
-0.076
-0.042
              The model is, on average, biased toward underestimations of observed ozone,
              especially in the Midwest and Northeast.

              The few regions which exhibit overestimated base year ozone are in the southern
              portion of the domain.  Baton Rouge, New Orleans, Birmingham, and Houston all
              have positive biases of at least 5 percent.

              Model performance is generally poorest in the Lake Michigan area probably due
              to the preponderance of shoreline monitors (where the highest ozone levels are
              often confined to within 1-3 km inland from the cool lake, that is, below the
              resolution of this analysis).  Gross errors in this region approach 30 percent.
       2.  Determination of Alternative Attainment Targets

       As indicated above, one of the primary purposes of the Tier 2 modeling was to determine
the number of areas projected to experience exceedances of the 1-hour standard in 2007.  These
areas were identified based on whether the highest daily maximum 1-hour value in the 2007 base
case was >=125 ppb. This "exceedance method" was criticized during the comment period as
potentially exaggerating the extent of the future year problem.  One commenter recommended
that EPA follow its own guidance (EPA, 1996) regarding attainment demonstrations for ozone
episode days associated with infrequent, severe meteorological conditions by allowing for
alternative attainment targets (i.e., values above 124 ppb). These alternative targets are allowed
for use in local attainment demonstrations to determine whether model-predicted peak ozone
values > 124 ppb can be considered as showing attainment in view of the statistical form of the
1-hour NAAQS in the case of especially severe meteorological conditions.

       To respond to this and similar comments we performed an analysis to identify the
alternative attainment targets appropriate for the episodes modeled.  This analysis was performed
for those 18 areas for which the meteorological severity has been calculated and ranked.
Alternative targets were not be calculated for the other areas.  The projected exceedance areas for
which this analysis was performed are: Atlanta, Baltimore, Baton Rouge, Birmingham, Chicago,
Greater Connecticut, Cleveland, Detroit, Houston, Huntington, Louisville, Milwaukee,
Muskegon, New York, Philadelphia, Pittsburgh, Providence, and St. Louis.

       Table IV-2 shows the alternative targets for those days/areas thought to be representative
of unusually severe meteorology. Some episode days are not shown because there are no
alternative targets for those days (i.e., the target remains 124 ppb for all areas). Each residual

                                           23

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nonattainment area (for which data was available to complete the alternate target analysis) had at
least one day in which the 2007 baseline maxima exceeded the attainment target, 124 ppb or
otherwise.
Table IV-2. Alternative attainment targets by non-attainment area and episode day.

Dallas
Houston
Baton Rouse
St. Louis
W. Lake Michigan
Lake Michigan
Birmingham
E. Lake Michigan
Louisville
Atlanta
Cincinnati
Pittsburgh
Washington B.C.
Baltimore
Philadelnhia
New York Citv
Greater CT
Boston
Providence
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6/19




130






130




130


6/20













130


130


6/21

130

















6/22




130














6/24




139














7/11






137


130









7/12


150

130

130
130

130









7/13




132


137
130


130







7/14




130


146



130




132
130
130
7/15




135




130

144

140
151
134
130


8/12






130












8/15









153

130







8/16






131


130









8/17






130


130









8/18



130





137









8/19


130
















8/20

130

















       3.  Estimation of Attainment/Nonattainment Using Relative Reduction Factors

       EPA received comments that recommended using relative reduction factors applied to
ambient design values as an approach to estimate future nonattainment. Specifically, the
commenters recommended that EPA follow draft guidance for demonstrating attainment of the 8-
hour NAAQS for such an analysis (EPA, 1999). In response, we calculated relative reduction
factors for the 2007 base case and control scenarios using the general methodology in this
guidance.  The exceptions to guidance are that: (a) relative reduction factors (RRF) were
calculated for the highest design value in a county rather than for all monitoring sites in a county
and (b) we used a cut-off of 80 ppb as  appropriate for considering 1-hour model predictions as
opposed to 70 ppb recommended in the guidance for 8-hour concentrations (see Appendix IV-5
for the rationale for selecting 80 ppb).  The county-specific relative reduction factors (2007-B
RRF, 2007-C RRF) were applied to both 1995-1997 and 1996-1998 design values for estimating
2007 base and 2007 control values (2007-B New DV, 2007-C New DV). The ambient design
values, adjustment factors based on modeling, and the resulting 2007 values are provided in
                                          24

-------
Appendix IV-6. The areas which have estimated 2007 base case design values >=125 ppb in
using either 1995-1997 and/or 1996-1998 design values are listed in Table IV-3.
Table IV-3. Areas which have estimated 2007 base case design values >=125 ppb.
Chicago, IL CMSA
Dallas, TX CMSA
Houston, TX CMSA
New York City, NY CMSA
Philadelphia, PA CMSA
Washington, DC-Baltimore, MD CMSA
Atlanta, GA CMSA
Baton Rouge, LA MSA
Beaumont, TX MSA
Grand Rapids, MI MSA
Hartford, CT MSA
New London, CT MSA
Houma, LA MSA
Longview, TX MSA
Sheboygan, WI MSA
Iberville Parish, LA
La Porte County, IN
Manitowoc County, WI
                                         25

-------
V. References

Alpine Geophysics, 1994: Technical Formulation Document: SARMAP/LMOS Emissions
Modeling System (EMS-95), Pittsburgh, PA.

Birth, T.L. and C.D. Geron, 1995: User's Guide to the Personal Computer Version of the
Biogenic Emissions Inventory System (PC-BEIS), Version 2.O., U.S. Environmental Protection
Agency, Research Triangle Park, NC.

Douglas, S.G., Hudischewskyj, A.B., and A.R. Alvarez, 1999: Preparation of Non-Emissions-
Related Inputs for Application of the UAM-V Modeling System to the Western U.S., ICF
Consulting Inc., Systems Applications International Inc., San Rafael CA.

E.H. Pechan and Associates, 1999: Procedures for Developing Base Year and Future Year Mass
and Modeling Inventories for the Tier 2 Final Rulemaking, Report to U.S. Environmental
Protection Agency, Research Triangle Park, NC.

EPA,  1991: Guideline For Regulatory Application of the Urban Airshed Model, Office of Air
Quality Planning and Standards, Technical Support Division, Source Receptor Analysis Branch,
Research Triangle Park, NC.

EPA,  1996: Guidance on Use of Modeled Results to Demonstrate Attainment, Office of Air
Quality Planning and Standards, EPA-454/B-95-007, Research Triangle Park, NC.

EPA,  1999: Draft Guidance on the Use of Models and Other Analyses in Attainment
Demonstrations for the 8-Hour Ozone NAAQS, Office of Air Quality Planning and Standards,
Research Triangle Park, NC.

Grell, G.A., Dudhia, I, and D.R. Stauffer, 1995: A Description of the Fifth-Generation Penn
State/NCARMesoscale Model (MM5), NCAR Technical Note, NCAR/TN-398, Boulder CO.

Houyoux, M., R. and J. M. Vukovich, 1999: Updates to the Sparse Matrix Operator Kernel
Emissions (SMOKE) Modeling System and Integration with Models-3. Presented at The
Emission Inventory: Regional Strategies for the Future, 26-28 October 1999, Raleigh, NC, Air &
Waste Management Association.

Lagouvardos,  K., Kallos, G., and V. Kotroni, 1997: Modeling and Analysis of Ozone and its
Precursors in the Northeast U.S.A. (Atmospheric Model Simulations), University of Athens,
Department of Physics, Laboratory of Meteorology, Athens.

OTAG, 1997. "OTAG Technical Support Document, Chapter 2: Regional Scale Modeling
Workgroup," Des Plaines, IL.
                                          26

-------
Pielke, R.A., W.R. Cotton, R.L. Walko, CJ. Tremback, W.A. Lyons, L.D. Grasso, M.E.
Nicholls, M.D. Moran, D.A. Wesley, TJ. Lee, and J.H. Copeland, 1992: A Comprehensive
Meteorological Modeling System - RAMS, Meteor. Atmos. Phys., 49. 69-91.

Sistla, Gopal, 1999: Personal communication.

Systems Applications International, 1996:  User's Guide to the Variable-Grid Urban Airshed
Model (UAM-V), SYSAPP-96-95/27r, San Rafael CA.
                                          27

-------
Appendix IV-1:
Areas with Predicted Exceedances in 2007 and/or 2030 Base Case Scenarios
CMSA/MSAs
Boston, MA CMSA
Chicago, IL CMSA
Cincinnati, OH CMSA
Cleveland, OH CMSA
Detroit, MI CMSA
Houston, TX CMSA
Milwaukee, WI CMSA
New York City, NY CMSA
Philadelphia, PA CMSA
Washington, DC-Baltimore, MD
CMSA
Atlanta, GA MSA
Barnstable, MA MSA
Baton Rouge, LA MSA
Benton Harbor, MI MSA
Biloxi,MS MSA
Birmingham, AL MSA
Buffalo, NY MSA
Canton, OH MSA
Charleston, WV MSA
Charlotte, NC MSA
Grand Rapids, MI MSA
Hartford, CT MSA
Houma, LA MSA
Huntington, WV MSA
Indianapolis, IN MSA
Jackson, MS MSA
2007 Base Case Exceedance:
June/July/August Episodes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
2030 Base Case Exceedance:
June/July Episodes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
Yes
                                IV-1-1

-------
Lafayette, LA MSA
Lakeland, FL MSA
Louisville, KY MSA
Macon, GA MSA
Melbourne, FL MSA
Memphis, TN MSA
Nashville, TN MSA
New London, CT MSA
New Orleans, LA MSA
Norfolk, VA MSA
Orlando, FL MSA
Pensacola, FL MSA
Pittsburgh, PA MSA
Portland, OR MSA
Providence, RI MSA
Richmond, VA MSA
Rochester, NY MSA
Rockford, IL MSA
St. Louis, MO MSA
Sarasota, FL MSA
Tampa, FL MSA
Toledo, OH MSA
Wheeling, WV MSA
York, PA MSA
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Not Applicable
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
No
No
Yes
Yes
Yes
Yes
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
IV-1-2

-------
Appendix IV-2:
Number of 12km Grid Cells Assigned to Each CMSA/MSA
CMSA/MSAs
Boston, MA CMSA
Chicago, IL CMSA
Cincinnati, OH CMSA
Cleveland, OH CMSA
Detroit, MI CMSA
Houston, TX CMSA
Milwaukee, WI CMSA
New York City, NY CMSA
Philadelphia, PA CMSA
Washington, DC-Baltimore, MD CMSA
Atlanta, GA MSA
Barnstable, MA MSA
Baton Rouge, LA MSA
Benton Harbor, MI MSA
Biloxi, MS MSA
Birmingham, AL MSA
Buffalo, NY MSA
Canton, OH MSA
Charleston, WV MSA
Charlotte, NC MSA
Grand Rapids, MI MSA
Hartford, CT MSA
Houma, LA MSA
Huntington, WV
Indianapolis, IN MSA
Jackson, MS MSA
Total Number of Grid Cells in Area
189
129
71
68
126
132
39
195
118
187
115
19
30
15
41
64
35
27
31
69
58
41
51
47
71
48
                              IV-2-1

-------
Lafayette, LA MSA
Lakeland, FL MSA
Louisville, KY MSA
Macon, GA MSA
Melbourne, FL MSA
Memphis, TN MSA
Nashville, TN MSA
New London, CT MSA
New Orleans, LA MSA
Norfolk, VA MSA
Orlando, FL MSA
Pensacola, FL MSA
Pittsburgh, PA MSA
Providence, RI MSA
Richmond, VA MSA
Rochester, NY MSA
Rockford, IL MSA
St. Louis, MO MSA
Sarasota, FL MSA
Tampa, FL MSA
Wheeling, WV MSA
York, PA MSA
56
21
45
37
32
58
78
12
96
60
61
34
80
20
66
71
32
127
23
56
21
20
IV-2-2

-------
Appendix IV-3:
Ozone Metrics for 2007 Base Case and Tier 2/Sulfur Control Case1

June/July/August
Episodes


Peak (ppb)
2007 Base
2007 Control

# Exceedance
2007 Base
2007 Control
% Change

Total Nonattainment (ppb)
2007 Base
2007 Control
% Change

Total ppb Decrease
Decrease, on Average

Total ppb Increase
Increase, on Average


2007 Base Case vs 2007 Tier 2/Sulfur Control Case
CMSA/MSA
Composite


N/A
N/A


1170
1056
-9.7%


12167
10384
-14.6%

1986
1.7

76
0.1

Atlanta


178
173


109
87
-20.2%


1713
1227
-28.4%

535
4.9

0
0.0

Barnstable


147
145


5
4
-20.0%


47
39
-17.0%

9
1.8

0
0.0

Baton Rouge


152
151


95
86
-9.5%


800
692
-13.5%

116
1.2

0
0.0

Benton Harbor


151
150


9
9
0.0%


93
89
-4.3%

5
0.6

0
0.0

Biloxi


142
141


30
24
-20.0%


123
94
-23.6%

32
1.1

0
0.0

Birmingham


138
133


12
7
-41.7%


68
37
-45.6%

41
3.4

1
0.1

Boston


145
143


12
9
-25.0%


88
65
-26.1%

24
2.0

0
0.0

Buffalo


133
133


o
J
3
0.0%


15
13
-13.3%

2
0.7

0
0.0

Canton


130
129


2
2
0.0%


7
4
-42.9%

3
1.5

0
0.0

             1 Note that values for total non-attainment, total ppb decrease, and total ppb increase are all rounded to the nearest "ppb".

                                                             IV-3-1

-------

June/July/August
Episodes


Peak (ppb)
2007 Base
2007 Control

# Exceedances
2007 Base
2007 Control
% Change

Total Nonattainment (ppb)
2007 Base
2007 Control
% Change

Total ppb Decrease
Decrease, on Average (ppb)

Total ppb Increase
Increase, on Average (ppb)

2007 Base Case vs 2007 Tier 2/Sulfur Control Case
CMSA/MSA
Charleston


139
138


2
2
0.0%


18
15
-16.7%

2
1.0

0
0.0
Charlotte


142
139


2
2
0.0%


25
19
-24.0%

6
3.0

0
0.0
Chicago


152
150


13
14
7.7%


119
123
3.4%

5
0.4

11
0.8
Cincinnati


138
137


8
7
-12.5%


44
42
-4.5%

6
0.8

3
0.4
Cleveland


136
138


4
3
-25.0%


20
19
-5.0%

4
1.0

2
0.5
Detroit


148
147


11
12
9.1%


106
124
17.0%

4
0.4

23
2.1
Grand Rapids


154
154


43
42
-2.3%


657
619
-5.8%

40
0.9

2
0.0
Hartford


172
170


17
17
0.0%


345
327
-5.2%

18
1.1

0
0.0
Houma


146
145


68
59
-13.2%


409
356
-13.0%

58
0.9

0
0.0
Houston


153
153


62
58
-6.5%


471
418
-11.3%

56
0.9

2
0.0
IV-3-2

-------

June/July/August
Episodes

Peak (ppb)
2007 Base
2007 Control

# Exceedances
2007 Base
2007 Control
% Change

Total Nonattainment (ppb)
2007 Base
2007 Control
% Change

Total ppb Decrease
Decrease, on Average (ppb)

Total ppb Increase
Increase, on Average (ppb)


2007 Base Case vs 2007 Tier 2/Sulfur Control Case
CMSA/MSA
Huntington

148
146


6
6
0.0%


65
54
-16.9%

10
1.7

0
0.0

Indianapolis

128
126


2
2
0.0%


4
2
-50.0%

3
1.5

0
0.0

Jackson

130
128


4
3
-25.0%


12
5
-58.3%

7
1.8

0
0.0

Lafayette

142
141


34
32
-5.9%


236
205
-13.1%

32
0.9

0
0.0

Lakeland

130
128


2
1
-50.0%


7
3
-57.1%

5
2.5

0
0.0

Louisville

150
149


21
21
0.0%


235
224
-4.7%

17
0.8

6
0.3

Macon

127
124


1
0
-100.0%


2
0
-100.0%

4
4.0

0
0.0

Melbourne

128
124


1
0
-100.0%


3
0
-100.0%

3
3.0

0
0.0

Memphis

150
148


6
5
-16.7%


78
68
-12.8%

12
2.0

0
0.0

Milwaukee

131
129


4
4
0.0%


10
10
0.0%

2
0.5

2
0.5

IV-3-3

-------

June/July/August
Episodes

Peak (ppb)
2007 Base
2007 Control

# Exceedances
2007 Base
2007 Control
% Change

Total Nonattainment (ppb)
2007 Base
2007 Control
% Change

Total ppb Decrease
Decrease, on Average (ppb)

Total ppb Increase
Increase, on Average (ppb)

2007 Base Case vs 2007 Tier 2/Sulfur Control Case
CMSA/MSA
Nashville

154
151


6
5
-16.7%


96
71
-26.0%

25
4.2

0
0.0
New London

159
157


18
19
5.6%


255
232
-9.0%

23
1.3

0
0.0
New Orleans

160
160


149
138
-7.4%


1252
1135
-9.3%

122
0.8

0
0.0
New York City

179
178


170
163
-4.1%


2064
1897
-8.1%

194
1.1

22
0.1
Norfolk

125
124


1
0
-100.0%


0
0
0.0%

1
1.0

0
0.0
Orlando

135
132


5
3
-40.0%


24
11
-54.2%

15
3.0

0
0.0
Pensacola

129
126


2
2
0.0%


8
3
-62.5%

5
2.5

0
0.0
Philadelphia

138
136


20
20
0.0%


110
79
-28.2%

32
1.6

0
0.0
Pittsburgh

127
124


2
0
-100.0%


4
0
-100.0%

5
2.5

0
0.0
IV-3-4

-------

June/July/August
Episodes

Peak (ppb)
2007 Base
2007 Control

# Exceedances
2007 Base
2007 Control
% Change

Total Nonattainment (ppb)
2007 Base
2007 Control
% Change

Total ppb Decrease
Decrease, on Average (ppb)

Total ppb Increase
Increase, on Average (ppb)

2007 Base Case vs 2007 Tier 2/Sulfur Control Case
CMSA/MSA
Providence

149
147


13
11
-15.4%


148
128
-13.5%

22
1.7

0
0.0
Richmond

151
149


11
11
0.0%


193
169
-12.4%

24
2.2

0
0.0
Rochester

125
124


1
0
-100.0%


0
0
0.0%

0
0.0

0
0.0
Rockford

129
128


1
1
0.0%


4
o
J
-25.0%

1
1.0

0
0.0
Sarasota

160
155


23
17
-26.1%


249
188
-24.5%

70
3.0

0
0.0
St. Louis

145
141


8
7
-12.5%


66
45
-31.8%

23
2.9

0
0.0
Tampa

171
167


77
69
-10.4%


1174
947
-19.3%

237
3.1

1
0.0
Toledo

132
131


1
1
0.0%


7
6
-14.3%

0
0.0

0
0.0
Washington-
Baltimore

155
154


72
67
-6.9%


690
576
-16.5%

123
1.7

1
0.0


Wheeling

128
126


2
1
-50.0%


3
1
-66.7%

o
3
1.5

0
0.0
IV-3-5

-------
Appendix IV-4:
Ozone Metrics for 2030 Base Case and Tier 2/Sulfur Control Case1
June/July Episodes


Peak (ppb)
2030 Base
2030 Control

# Exceedances
2030 Base
2030 Control
% Change

Total Nonattainment (ppb)
2030 Base
2030 Control
% Change

Total ppb Decrease
Decrease, on Average (ppb)

Total ppb Increase
Increase, on Average (ppb)




# Exceedances
2007 Base
2007 Control
% Change
2030 Base Case vs 2030 Tier 2/Sulfur Control Case
CMSA/MSA
Composite

N/A
N/A


708
479
-32.3%


8122
5165
-36.4%

4248
6.0

276
0.4
Atlanta

173
162


52
20
-61.5%


772
182
-76.4%

938
18.0

0
0.0
Barnstable

153
146


5
4
-20.0%


71
37
-47.9%

35
7.0

0
0.0
Baton Rouge

154
149


70
55
-21.4%


706
499
-29.3%

237
3.4

0
0.0
Benton Harbor

154
152


9
9
0.0%


110
91
-17.3%

19
2.1

0
0.0
Biloxi

138
136


23
12
-47.8%


91
38
-58.2%

71
3.1

0
0.0
Birmingham

139
131


7
2
-71.4%


45
11
-75.6%

55
7.9

1
0.1
Boston

148
138


15
5
-66.7%


115
28
-75.7%

129
8.6

0
0.0
Buffalo

134
131


3
2
-33.3%


17
10
-41.2%

7
2.3

0
0.0
Canton

134
127


3
1
-66.7%


16
2
-87.5%

19
6.3

0
0.0

2007 Exceedances in June/July Episodes For Comparison to 2030 Exceedance

Composite

563
514
-8.7%
Atlanta

42
33
-21.4%
Barnstable

5
4
-20.0%
Baton Rouge

57
52
-8.8%
Benton Harbor

9
9
0.0%
Biloxi

17
13
-23.5%
Birmingham

6
3
-50.0%
Boston

12
9
-25.0%
Buffalo

3
3
0.0%
Canton

2
2
0.0%
              1 Note that values for total non-attainment, total ppb decrease, and total ppb increase are all rounded to the nearest "ppb".

                                                                     IV-4-1

-------

June/July Episodes


Peak (ppb)
2030 Base
2030 Control

# Exceedances
2030 Base
2030 Control
% Change

Total Nonattainment (ppb)
2030 Base
2030 Control
% Change

Total ppb Decrease
Decrease, on Average (ppb)

Total ppb Increase
Increase, on Average (ppb)




# Exceedances
2007 Base
2007 Control
% Change

2030 Base Case vs 2030 Tier 2/Sulfur Control Case
CMSA/MSA
Charlotte

148
140


2
1
-50.0%


35
15
-57.1%

22
11.0

0
0.0
Chicago

156
150


20
20
0.0%


166
197
18.7%

37
1.8

81
4.0
Cleveland

138
143


6
4
-33.3%


37
24
-35.1%

23
3.8

5
0.8
Detroit

152
150


14
14
0.0%


138
182
31.9%

20
1.4

69
4.9
Grand Rapids

156
152


33
26
-21.2%


576
429
-25.5%

162
4.9

0
0.0
Hartford

176
168


15
13
-13.3%


370
283
-23.5%

92
6.1

0
0.0
Houma

133
133


10
9
-10.0%


38
27
-28.9%

12
1.2

0
0.0
Houston

152
152


34
24
-29.4%


294
215
-26.9%

91
2.7

1
0.0
Jackson

132
120


1
0
-100.0%


7
0
-100.0%

18
18.0

0
0.0
Lafayette

146
144


25
17
-32.0%


171
117
-31.6%

63
2.5

0
0.0

2007 Exceedances in June/July Episodes For Comparison to 2030 Exceedance

Charlotte

2
2
0.0%
Chicago

13
14
7.7%
Cleveland

4
3
-25.0%
Detroit

11
12
9.1%
Grand Rapids

30
29
-3.3%
Hartford

13
13
0.0%
Houma

6
6
0.0%
Houston

31
30
-3.2%
Jackson

1
0
-100.0%
Lafayette

15
15
0.0%
IV-4-2

-------

June/July Episodes


Peak (ppb)
2030 Base
2030 Control

# Exceedances
2030 Base
2030 Control
% Change

Total Nonattainment (ppb)
2030 Base
2030 Control
% Change

Total ppb Decrease
Decrease, on Average (ppb)

Total ppb Increase
Increase, on Average (ppb)




# Exceedances
2007 Base
2007 Control
% Change

2030 Base Case vs 2030 Tier 2/Sulfur Control Case
CMSA/MSA
Louisville

137
134


5
3
-40.0%


26
22
-15.4%

18
3.6

8
1.6
Memphis

155
148


5
4
-20.0%


95
67
-29.5%

30
6.0

0
0.0
Milwaukee

136
136


4
3
-25.0%


28
23
-17.9%

15
3.8

4
1.0
Nashville

157
142


3
1
-66.7%


43
17
-60.5%

45
15.0

0
0.0
New London

163
154


18
16
-11.1%


312
207
-33.7%

109
6.1

0
0.0
New Orleans

155
154


43
31
-27.9%


358
271
-24.3%

106
2.5

0
0.0
New York City

184
177


152
117
-23.0%


2247
1695
-24.6%

780
5.1

95
0.6
Philadelphia

140
131


27
10
-63.0%


181
35
-80.7%

205
7.6

0
0.0
Pittsburgh

127
127


1
1
0.0%


2
2
0.0%

3
3.0

9
9.0

2007 Exceedances in June/July Episodes For Comparison to 2030 Exceedance

Louisville

2
2
0.0%
Memphis

5
4
-20.0%
Milwaukee

3
3
0.0%
Nashville

2
1
-50.0%
New London

16
16
0.0%
New Orleans

37
32
-13.5%
New York City

123
118
-4.1%
Philadelphia

19
19
0.0%
Pittsburgh

0
0
0.0%
IV-4-3

-------

June/July Episodes


Peak (ppb)
2030 Base
2030 Control

# Exceedances
2030 Base
2030 Control
% Change

Total Nonattainment (ppb)
2030 Base
2030 Control
% Change

Total ppb Decrease
Decrease, on Average (ppb)

Total ppb Increase
Increase, on Average (ppb)




# Exceedances
2007 Base
2007 Control
% Change

2030 Base Case vs 2030 Tier 2/Sulfur Control Case
CMSA/MSA
Providence

152
142


15
11
-26.7%


193
92
-52.3%

112
7.5

0
0.0
Richmond

126
119


2
0
-100.0%


1
0
-100.0%

15
7.5

0
0.0
Rochester

127
125


2
2
0.0%


3
1
-66.7%

2
1.0

0
0.0
Rockford

133
131


2
1
-50.0%


11
6
-45.5%

7
3.5

0
0.0
Sarasota

153
142


3
2
-33.3%


42
19
-54.8%

29
9.7

0
0.0
St. Louis

139
131


11
2
-81.8%


61
7
-88.5%

116
10.5

0
0.0
Tampa

162
153


21
11
-47.6%


294
141
-52.0%

234
11.1

0
0.0
Toledo

133
133


1
1
0.0%


8
8
0.0%

0
0.0

0
0.0
Washington-
Baltimore

154
146


45
25
-44.4%


442
165
-62.7%

363
8.1

3
0.1
York

125
117


1
0
-100.0%


0
0
0.0%

9
9.0

0
0.0

2007 Exceedances in June/July Episodes For Comparison to 2030 Exceedance


Providence

13
11
-15.4%
Richmond

0
0
0.0%
Rochester

1
0
-100.0%
Rockford

1
1
0.0%
Sarasota

2
2
0.0%
St. Louis

5
4
-20.0%
Tampa

15
13
-13.3%
Toledo

1
1
0.0%
Washington-
Baltimore

39
35
-10.3%
York

0
0
0.0%
IV-4-4

-------
Appendix IV-5: Limiting Modeled One-Hour Daily Maxima used in
Calculation of Relative Reduction Factors

       As part of the identification of the need for the proposed Tier 2 standards, EPA is
planning to use a rollback approach to link future-year model ozone changes to present-day
ozone design values, thereby allowing an assessment of an area's future year nonattainment
status.  The specific equation used in the analysis is:

       DVF; = RRF; * DVQ

where  DVF; = the future design value predicted for site i,
       RRF; = the relative reduction factor calculated near site i, and
       DVC; = the current design value monitored at site i.

       One of the more important details in an accurate calculation of episode-average RRF; is to
ensure that the calculated factor represents ozone improvements on high ozone days. If ozone
predicted near a monitor on a particular day is very much less than the design value, the model
predictions for that day could be unresponsive to controls (e.g., location  could be upwind of
controls for a given meteorological situation). EPA draft guidance on eight-hour attainment
demonstrations recommended limiting RRF calculations to those instances where the daily
maximum eight-hour model concentration in a nearby grid cell exceeded 70 ppb. This threshold
was set based on 90 days of modeling data (at 158 sites) investigating the relationship between
the RRF and the base magnitude.

       The Tier 2 modeling analyses will attempt to project future-year one-hour design values,
therefore a separate rollback threshold will be needed. Two simple approaches were used to
derive an appropriate cutoff. The first approach is based on the assumption that peak eight-hour
ozone concentrations are generally  85 percent of their one-hour counterparts. Using this
methodology, the 70 ppb threshold identified as part of the 8-hour analysis discussed above
would translate to a 80-85 ppb  value (82.4).

       The second approach looked at the relationship between one-hour model response (from a
preliminary Tier 2 strategy run) and base model one-hour ozone at every grid cell of the  domain
over the July 8th - July  15th episode.  As  an example, Figure 1 shows a scatterplot of the two
fields for July 14th for an across-the-board NOx simulation.  There  is a clear relationship between
RRF and base ozone up to about 70-85 ppb. The largest reductions (RRFs of approximately 0.8)
appear to occur in conjunction  with base ozone values greater than about 80 ppb. Figure 2 shows
the same style plot for  a VOC control run. Again, the relationship between base ozone and
ozone response appears to hold only to about 70-85 ppb.
                                        IV-5-1

-------
Figure 1. Scatterplot comparing base model ozone concentrations (x-axis) and relative reduction
factor (y-axis) for each grid cell on July 14th, 1995.  The control simulation was an across-the-
board NOx simulation.
Figure 2. Scatterplot comparing base model ozone concentrations (x-axis) and relative reduction
factor (y-axis) for each grid cell on July 14th, 1995.  The control simulation was an across-the-
board VOC simulation.
                                         IV-5-2

-------
Appendix IV-6: Rollback Calculations

1995-1997 Design Values1
State
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Wisconsin
Kentucky
Kentucky
Kentucky
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Texas
Texas
Texas
Texas
Texas
Michigan
Michigan
Michigan
County
Cook
Du Page
Kane
Lake
McHenry
Will
Lake
Porter
Kenosha
Boone
Campbell
Kenton
Butler
Clermont
Hamilton
Warren
Ashtabula
Cuyahoga
Geauga
Lake
Lorain
Medina
Portage
Summit
Collin
Dallas
Denton
Ellis
Tarrant
Genesee
Lenawee
Macomb
1995-97
Des
Value
127
103
116
116
108
108
117
124
129
108
115
114
125
116
119
124
105
108
112
119
101
110
114
113
132
134
139
118
133
99
104
124
2007-
B
RRF
0.911
0.928
0.919
0.915
0.913
0.919
0.915
0.912
0.921
0.839
0.895
0.885
0.876
0.880
0.910
0.895
0.907
0.916
0.894
0.915
0.915
0.904
0.906
0.916
0.941
0.946
0.940
0.967
0.945
0.902
0.915
0.914
2007-B
New
DV
115
95
106
106
98
99
107
113
118
90
102
100
109
102
108
110
95
98
100
108
92
99
103
103
124
126
130
114
125
89
95
113
2007-
C
RRF
0.905
0.927
0.925
0.912
0.917
0.914
0.910
0.906
0.922
0.830
0.889
0.878
0.865
0.872
0.905
0.885
0.896
0.911
0.883
0.906
0.910
0.892
0.893
0.906
0.930
0.937
0.925
0.946
0.930
0.891
0.907
0.918
2007-C
New
DV
114
95
107
105
99
98
106
112
118
89
102
100
108
101
107
109
94
98
98
107
91
98
101
102
122
125
128
111
123
88
94
113
CMSA Name
(if applicable)
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Dallas
Dallas
Dallas
Dallas
Dallas
Detroit
Detroit
Detroit
MSA Name
(if applicable)
CHICAGO, IL
CHICAGO, IL
CHICAGO, IL
CHICAGO, IL
CHICAGO, IL
CHICAGO, IL
GARY, IN
GARY, IN
KENOSHA, Wl
CINCINNATI, OH-KY-IN
CINCINNATI, OH-KY-IN
CINCINNATI, OH-KY-IN
HAMILTON, OH
CINCINNATI, OH-KY-IN
CINCINNATI, OH-KY-IN
CINCINNATI, OH-KY-IN
CLEVELAND
CLEVELAND
CLEVELAND
CLEVELAND
CLEVELAND
CLEVELAND
AKRON, OH
AKRON, OH
DALLAS, TX
DALLAS, TX
DALLAS, TX
DALLAS, TX
FORT WORTH, TX
FLINT, Ml
ANN ARBOR, Ml
DETROIT, Ml
      i ***
are below 80 ppb.
                  county where RRF is not defined because all current 1-hr daily maximum model ozone values (for all 30 days)
                                                     IV-6-1

-------
Michigan
Michigan
Michigan
Michigan
Texas
Texas
Texas
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Connecticut
Connecticut
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Delaware
Maryland
New Jersey
New Jersey
New Jersey
New Jersey
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
D.C.
Oakland
St Clair
Washtenaw
Wayne
Brazoria
Galveston
Harris
Milwaukee
Ozaukee
Racine
Washington
Waukesha
Fairfield
New Haven
Bergen
Essex
Hudson
Hunterdon
Mercer
Middlesex
Monmouth
Morris
Ocean
Union
Bronx
Out chess
Kings
New York
Orange
Putnam
Queens
Richmond
Suffolk
Westchester
New Castle
Cecil
Atlantic
Camden
Cumberland
Gloucester
Bucks
Delaware
Montgomery
Philadelphia
Washington
117
119
104
114
148
182
189
126
127
119
106
109
138
157
122
114
120
119
131
139
138
124
149
109
123
113
124
121
115
122
125
137
138
121
139
152
124
137
115
128
137
126
122
130
125
0.913
0.924
0.924
0.923
0.975
0.968
0.944
0.918
0.919
0.921
0.895
0.900
0.937
0.935
0.954
0.938
0.938
0.921
0.917
0.921
0.934
0.898
0.908
0.924
0.954
0.799
0.926
0.926
0.860
0.893
0.924
0.934
0.923
0.941
0.849
0.842
0.909
0.912
0.869
0.885
0.910
0.882
0.890
0.903
0.906
106
109
96
105
144
176
178
115
116
109
94
98
129
146
116
106
112
109
120
128
128
111
135
100
117
90
114
112
98
108
115
127
127
113
118
127
112
124
99
113
124
111
108
117
113
0.922
0.922
0.917
0.925
0.968
0.961
0.944
0.914
0.915
0.921
0.892
0.897
0.934
0.930
0.954
0.934
0.934
0.913
0.911
0.916
0.927
0.889
0.899
0.920
0.954
0.794
0.921
0.921
0.858
0.891
0.917
0.932
0.918
0.938
0.838
0.831
0.899
0.901
0.859
0.877
0.904
0.874
0.886
0.899
0.902
107
109
95
105
143
174
178
115
116
109
94
97
128
146
116
106
112
108
119
127
127
110
133
100
117
89
114
111
98
108
114
127
126
113
116
126
111
123
98
112
123
110
108
116
112
Detroit
Detroit
Detroit
Detroit
Houston
Houston
Houston
Milwaukee
Milwaukee
Milwaukee
Milwaukee
Milwaukee
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington-
Baltimore
DETROIT, Ml
DETROIT, Ml
ANN ARBOR, Ml
DETROIT, Ml
BRAZORIA, TX
GALVESTON, TX
HOUSTON, TX
MILWAUKEE, Wl
MILWAUKEE, Wl
RACINE, Wl
MILWAUKEE, Wl
MILWAUKEE, Wl
NEW HAVEN, CT
NEW HAVEN, CT
BERGEN, NJ
NEWARK, NJ
JERSEY CITY, NJ
MIDDLESEX, NJ
TRENTON, NJ
MIDDLESEX, NJ
MONMOUTH, NJ
NEWARK, NJ
MONMOUTH, NJ
NEWARK, NJ
NEW YORK, NY
DUTCHESS COUNTY, NY
NEW YORK, NY
NEW YORK, NY
NEWBURGH, NY-PA
NEW YORK, NY
NEW YORK, NY
NEW YORK, NY
NASSAU-SUFFOLK, NY
NEW YORK, NY
WILMINGTON, DE-MD
WILMINGTON, DE-MD
ATLANTIC-CAPE MAY, NJ
PHILADELPHIA, PA-NJ
VINELAND, NJ
PHILADELPHIA, PA-NJ
PHILADELPHIA, PA-NJ
PHILADELPHIA, PA-NJ
PHILADELPHIA, PA-NJ
PHILADELPHIA, PA-NJ
WASHINGTON, DC
IV-6-2

-------
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
New York
New York
New York
Pennsylvania
Pennsylvania
Pennsylvania
Wisconsin
Wisconsin
North Carolina
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
South Carolina
Anne Arundel
Baltimore
Baltimore City
Calvert
Carroll
Charles
Harford
Montgomery
Prince Georges
Alexandria City
Arlington
Fairfax
Fauquier
Prince William
Stafford
Albany
Saratoga
Schenectady
Lehigh
Northampton
Blair
Outagamie
Winnebago
Buncombe
De Kalb
Douglas
Fulton
Gwinnett
Paulding
Rockdale
Richmond
Aiken
142
130
137
105
115
118
145
118
132
124
123
124
97
110
110
105
101
94
114
109
114
98
98
86
136
140
143
121
112
145
118
104
0.892
0.890
0.899
0.848
0.882
0.857
0.881
0.892
0.892
0.909
0.909
0.904
0.871
0.877
0.879
0.833
0.846
0.815
0.884
0.893
0.851
0.864
0.902
0.825
0.894
0.902
0.899
0.894
0.849
0.875
0.886
0.861
126
115
123
89
101
101
127
105
117
112
111
112
84
96
96
87
85
76
100
97
97
84
88
70
121
126
128
108
95
126
104
89
0.883
0.880
0.892
0.835
0.871
0.844
0.871
0.884
0.883
0.906
0.906
0.896
0.860
0.867
0.867
0.819
0.829
0.808
0.877
0.883
0.839
0.854
0.892
0.803
0.872
0.882
0.876
0.860
0.826
0.839
0.857
0.841
125
114
122
87
100
99
126
104
116
112
111
111
83
95
95
86
83
75
99
96
95
83
87
69
118
123
125
104
92
121
101
87
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore

















BALTIMORE.MD
BALTIMORE.MD
BALTIMORE.MD
WASHINGTON, DC
BALTIMORE.MD
WASHINGTON, DC
BALTIMORE.MD
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
ALBANY, NY
ALBANY, NY
ALBANY, NY
ALLENTOWN, PA
ALLENTOWN, PA
ALTOONA, PA
APPLETON, Wl
APPLETON, Wl
ASHEVILLE, NC
ATLANTA, GA
ATLANTA, GA
ATLANTA, GA
ATLANTA, GA
ATLANTA, GA
ATLANTA, GA
AUGUSTA, GA-SC
AUGUSTA, GA-SC
IV-6-3

-------
South Carolina
Texas
Maine
Massachusetts
Louisiana
Louisiana
Louisiana
Louisiana
Texas
Texas
Michigan
Mississippi
Mississippi
Alabama
Alabama
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
New Hampshire
New York
New York
Vermont
Ohio
Iowa
Illinois
West Virginia
South Carolina
South Carolina
North Carolina
North Carolina
North Carolina
South Carolina
Tennessee
Kentucky
South Carolina
Georgia
Ohio
Ohio
Ohio
Ohio
Texas
Illinois
Iowa
Edgefield
Travis
Penobscot
Barnstable
Ascension
East Baton Rouge
Livingston
West Baton Rouge
Jefferson
Orange
Berrien
Hancock
Jackson
Jefferson
Shelby
Bristol
Essex
Middlesex
Plymouth
Suffolk
Worcester
Rocking ham
Erie
Niagara
Chittenden
Stark
Linn
Champaign
Kanawha
Berkeley
Charleston
Lincoln
Mecklenburg
Rowan
York
Hamilton
Christian
Richland
Muscogee
Delaware
Franklin
Licking
Madison
Nueces
Rock Island
Scott
93
104
95
131
121
131
127
114
139
121
119
105
109
132
127
138
113
109
102
95
108
130
91
102
85
107
74
94
110
94
102
105
123
119
114
113
101
107
108
99
107
115
112
115
83
95
0.851
0.936
0.942
0.911
0.990
0.981
0.988
0.979
0.981
0.987
0.907
0.985
0.998
0.879
0.882
0.883
0.954
0.917
0.909
0.912
0.910
0.959
0.922
0.920
***
0.908
0.936
0.869
0.891
0.900
0.873
0.872
0.876
0.846
0.874
0.862
0.751
0.893
0.900
0.881
0.897
0.878
0.872
***
0.924
0.920
79
97
89
119
119
128
125
111
136
119
107
103
108
116
112
121
107
99
92
86
98
124
83
93
***
97
69
81
97
84
89
91
107
100
99
97
75
95
97
87
95
100
97
***
76
87
0.827
0.919
0.932
0.900
0.982
0.970
0.981
0.965
0.975
0.979
0.897
0.975
0.988
0.857
0.860
0.872
0.945
0.908
0.897
0.902
0.900
0.951
0.916
0.911
***
0.893
0.927
0.857
0.882
0.876
0.852
0.851
0.855
0.826
0.852
0.829
0.742
0.861
0.867
0.869
0.894
0.864
0.861
***
0.920
0.915
76
95
88
117
118
127
124
110
135
118
106
102
107
113
109
120
106
98
91
85
97
123
83
92
***
95
68
80
96
82
86
89
105
98
97
93
74
92
93
86
95
99
96
***
76
86














































AUGUSTA, GA-SC
AUSTIN, TX
BANGOR, ME
BARNSTABLE, MA
BATON ROUGE, LA
BATON ROUGE, LA
BATON ROUGE, LA
BATON ROUGE, LA
BEAUMONT, TX
BEAUMONT, TX
BENTON HARBOR, Ml
BILOXI, MS
BILOXI, MS
BIRMINGHAM, AL
BIRMINGHAM, AL
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BUFFALO, NY
BUFFALO, NY
BURLINGTON, VT
CANTON, OH
CEDAR RAPIDS, I A
CHAMPAIGN, IL
CHARLESTON, WV
CHARLESTON, SC
CHARLESTON, SC
CHARLOTTE, NC-SC
CHARLOTTE, NC-SC
CHARLOTTE, NC-SC
CHARLOTTE, NC-SC
CHATTANOOGA, TN-GA
CLARKSVILLE, TN-KY
COLUMBIA, SC
COLUMBUS, GA-AL
COLUMBUS, OH
COLUMBUS, OH
COLUMBUS, OH
COLUMBUS, OH
CORPUS CHRISTI, TX
DAVENPORT, IA-IL
DAVENPORT, IA-IL
IV-6-4

-------
Florida
Ohio
Ohio
Ohio
Ohio
Alabama
Alabama
Illinois
Iowa
Iowa
Delaware
Indiana
New York
Pennsylvania
Indiana
Indiana
Indiana
Kentucky
North Carolina
Alabama
Florida
Florida
Indiana
Indiana
Florida
Michigan
Michigan
Michigan
Michigan
Wisconsin
North Carolina
North Carolina
North Carolina
North Carolina
South Carolina
South Carolina
South Carolina
South Carolina
Pennsylvania
Pennsylvania
Connecticut
Connecticut
Connecticut
Connecticut
North Carolina
North Carolina
Louisiana
Volusia
Clark
Greene
Miami
Montgomery
Lawrence
Morgan
Macon
Polk
Warren
Kent
Elkhart
Chemung
Erie
Posey
Vanderburgh
Warrick
Henderson
Cumberland
Colbert
Lee
St Lucie
Allen
De Kalb
Alachua
Allegan
Kent
Muskegon
Ottawa
Brown
Da vie
Forsyth
Guilford
Pitt
Anderson
Cherokee
Pickens
Spartanburg
Dauphin
Perry
Hartford
Litchfield
Middlesex
Tolland
Alexander
Caldwell
Lafourche
89
118
111
110
112
98
114
100
82
74
124
113
88
105
99
114
113
108
106
83
83
82
106
82
101
137
124
136
113
108
105
115
109
104
114
106
107
117
113
103
138
120
135
127
94
97
127
0.955
0.876
0.865
0.875
0.874
0.813
0.876
0.859
0.889
0.881
0.901
0.884
0.900
0.908
0.875
0.873
0.865
0.865
0.878
0.812
0.989
0.997
0.906
0.904
0.923
0.917
0.910
0.919
0.919
0.898
0.809
0.839
0.864
0.900
0.870
0.860
0.847
0.853
0.901
0.844
0.923
0.897
0.939
0.919
0.822
0.869
0.987
85
103
95
96
97
79
99
85
72
65
111
99
79
95
86
99
97
93
93
67
82
81
96
74
93
125
112
124
103
97
84
96
94
93
99
91
90
99
101
86
127
107
126
116
77
84
125
0.928
0.863
0.852
0.860
0.864
0.797
0.861
0.846
0.874
0.867
0.887
0.873
0.884
0.897
0.865
0.862
0.855
0.856
0.851
0.796
0.966
0.975
0.893
0.890
0.900
0.911
0.904
0.912
0.913
0.889
0.790
0.815
0.840
0.882
0.846
0.840
0.823
0.829
0.881
0.825
0.917
0.888
0.933
0.910
0.807
0.847
0.980
82
101
94
94
96
78
98
84
71
64
110
98
77
94
85
98
96
92
90
66
80
79
94
72
90
124
112
124
103
95
82
93
91
91
96
89
88
96
99
85
126
106
126
115
75
82
124















































DAYTONA BEACH, FL
DAYTON, OH
DAYTON, OH
DAYTON, OH
DAYTON, OH
DECATUR, AL
DECATUR, AL
DECATUR, IL
DES MOINES, IA
DES MOINES, IA
DOVER, DE
ELKHART, IN
ELMIRA, NY
ERIE, PA
EVANSVILLE, IN-KY
EVANSVILLE, IN-KY
EVANSVILLE, IN-KY
EVANSVILLE, IN-KY
FAYETTEVILLE, NC
FLORENCE, AL
FORT MEYERS, FL
FORT PIERCE-, FL
FORT WAYNE, IN
FORT WAYNE, IN
GAINESVILLE, FL
GRAND RAPIDS-MUSKEGON, Ml
GRAND RAPIDS-MUSKEGON, Ml
GRAND RAPIDS-MUSKEGON, Ml
GRAND RAPIDS-MUSKEGON, Ml
GREEN BAY, Wl
GREENSBORO, NC
GREENSBORO, NC
GREENSBORO, NC
GREENVILLE, NC
GREENVILLE, SC
GREENVILLE, SC
GREENVILLE, SC
GREENVILLE, SC
HARRISBURG, PA
HARRISBURG, PA
HARTFORD, CT
HARTFORD, CT
HARTFORD, CT
HARTFORD, CT
HICKORY, NC
HICKORY, NC
HOUMA, LA
IV-6-5

-------
Kentucky
Kentucky
Ohio
West Virginia
Alabama
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Mississippi
Mississippi
Tennessee
Florida
Florida
New York
Wisconsin
Tennessee
Pennsylvania
Michigan
Kansas
Missouri
Missouri
Missouri
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Indiana
Louisiana
Louisiana
Florida
Pennsylvania
Michigan
Michigan
Kentucky
Kentucky
Kentucky
Ohio
Nebraska
Arkansas
Texas
Indiana
Indiana
Kentucky
Boyd
Greenup
Lawrence
Cabell
Madison
Hamilton
Hancock
Johnson
Madison
Marion
Morgan
Hinds
Madison
Madison
Duval
St Johns
Chautauqua
Rock
Sullivan
Cambria
Kalamazoo
Wyandotte
Clay
Jackson
Platte
Anderson
Blount
Knox
Loudon
Sevier
Tippecanoe
Lafayette
Calcasieu
Polk
Lancaster
Clinton
Ingham
Fayette
Jessamine
Scott
Allen
Lancaster
Pulaski
Gregg
Clark
Floyd
Bullitt
122
114
113
122
102
116
120
102
112
115
103
97
89
64
116
91
104
103
111
102
106
113
128
88
116
110
117
120
112
111
104
109
116
99
125
88
97
101
98
101
106
68
108
139
125
125
116
0.858
0.842
0.837
0.862
0.871
0.900
0.902
0.863
0.901
0.904
0.887
0.944
0.968
0.826
0.965
0.953
0.910
0.898
0.861
0.875
0.897
0.950
0.947
0.933
0.946
0.834
0.840
0.866
0.846
0.796
0.891
0.974
0.987
0.972
0.895
0.914
0.916
0.885
0.878
0.831
0.892
0.938
0.952
0.977
0.889
0.895
0.874
104
95
94
105
88
104
108
88
100
103
91
91
86
52
111
86
94
92
95
89
95
107
121
82
109
91
98
103
94
88
92
106
114
96
111
80
88
89
86
83
94
63
102
135
111
111
101
0.847
0.832
0.826
0.851
0.848
0.889
0.890
0.853
0.888
0.896
0.881
0.921
0.960
0.811
0.936
0.925
0.899
0.890
0.847
0.864
0.886
0.941
0.935
0.923
0.937
0.813
0.816
0.844
0.824
0.778
0.879
0.964
0.979
0.947
0.882
0.904
0.907
0.872
0.866
0.819
0.880
0.925
0.927
0.960
0.883
0.891
0.868
103
94
93
103
86
103
106
87
99
103
90
89
85
51
108
84
93
91
94
88
93
106
119
81
108
89
95
101
92
86
91
105
113
93
110
79
87
88
84
82
93
62
100
133
110
111
100















































HUNTINGTON, WV-KY-OH
HUNTINGTON, WV-KY-OH
HUNTINGTON, WV-KY-OH
HUNTINGTON, WV-KY-OH
HUNTSVILLE, AL
INDIANAPOLIS, IN
INDIANAPOLIS, IN
INDIANAPOLIS, IN
INDIANAPOLIS, IN
INDIANAPOLIS, IN
INDIANAPOLIS, IN
JACKSON, MS
JACKSON, MS
JACKSON, TN
JACKSONVILLE, FL
JACKSONVILLE, FL
JAMESTOWN, NY
JANESVILLE-BELOIT, Wl
JOHNSON CITY, TN-VA
JOHNSTOWN, PA
KALAMAZOO, Ml
KANSAS CITY, MO-KS
KANSAS CITY, MO-KS
KANSAS CITY, MO-KS
KANSAS CITY, MO-KS
KNOXVILLE, TN
KNOXVILLE, TN
KNOXVILLE, TN
KNOXVILLE, TN
KNOXVILLE, TN
LAFAYETTE, IN
LAFAYETTE, LA
LAKE CHARLES, LA
LAKELAND, FL
LANCASTER, PA
LANSING, Ml
LANSING, Ml
LEXINGTON, KY
LEXINGTON, KY
LEXINGTON, KY
LIMA, OH
LINCOLN, NE
LITTLE ROCK, AR
LONGVIEW, TX
LOUISVILLE, KY-IN
LOUISVILLE, KY-IN
LOUISVILLE, KY-IN
IV-6-6

-------
Kentucky
Kentucky
New Hampshire
Georgia
Wisconsin
New Hampshire
Florida
Arkansas
Mississippi
Tennessee
Minnesota
Minnesota
Minnesota
Wisconsin
Alabama
Louisiana
Alabama
Alabama
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Connecticut
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Virginia
Virginia
Oklahoma
Oklahoma
Oklahoma
Nebraska
Florida
Florida
Florida
Kentucky
Ohio
West Virginia
Florida
Illinois
Pennsylvania
Pennsylvania
Jefferson
Oldham
Hillsborough
Bibb
Dane
Merrimack
Brevard
Crittenden
De Soto
Shelby
Anoka
Dakota
Washington
St Croix
Mobile
Ouachita
Elmore
Montgomery
Davidson
Dickson
Rutherford
Sumner
Williamson
Wilson
New London
Jefferson
Orleans
St Bernard
St Charles
St James
St John The Baptis
Hampton City
Suffolk City
Cleveland
Me Clain
Oklahoma
Douglas
Orange
Osceola
Seminole
Daviess
Washington
Wood
Escambia
Peoria
Allegheny
Beaver
120
112
111
122
97
98
86
122
131
128
106
91
103
88
111
94
102
92
110
120
95
124
110
108
144
107
96
98
115
119
114
109
108
102
95
110
88
106
96
95
108
110
116
113
95
133
105
0.892
0.871
0.902
0.808
0.921
0.864
0.986
0.914
0.941
0.943
0.913
0.918
0.948
0.938
0.920
0.976
0.894
0.891
0.908
0.691
0.870
0.887
0.813
0.858
0.930
0.970
0.969
0.982
0.978
0.990
0.982
0.912
0.908
0.972
0.993
0.977
0.926
0.985
0.983
0.975
0.846
0.843
0.830
0.967
0.899
0.895
0.932
107
97
100
98
89
84
84
111
123
120
96
83
97
82
102
91
91
81
99
82
82
110
89
92
133
103
93
96
112
117
111
99
98
99
94
107
81
104
94
92
91
92
96
109
85
119
97
0.888
0.864
0.895
0.783
0.909
0.858
0.960
0.904
0.928
0.935
0.912
0.915
0.938
0.924
0.904
0.965
0.871
0.867
0.889
0.681
0.846
0.869
0.795
0.841
0.924
0.962
0.963
0.975
0.972
0.982
0.975
0.904
0.900
0.952
0.972
0.959
0.915
0.956
0.955
0.945
0.838
0.833
0.821
0.949
0.890
0.885
0.921
106
96
99
95
88
84
82
110
121
119
96
83
96
81
100
90
88
79
97
81
80
107
87
90
133
102
92
95
111
116
111
98
97
97
92
105
80
101
91
89
90
91
95
107
84
117
96















































LOUISVILLE, KY-IN
LOUISVILLE, KY-IN
LOWELL, MA-NH
MACON, GA
MADISON, Wl
MANCHESTER, NH
MELBOURNE, FL
MEMPHIS, TN-AR-MS
MEMPHIS, TN-AR-MS
MEMPHIS, TN-AR-MS
MINNEAPOLIS, MN-WI
MINNEAPOLIS, MN-WI
MINNEAPOLIS, MN-WI
MINNEAPOLIS, MN-WI
MOBILE, AL
MONROE, LA
MONTGOMERY, AL
MONTGOMERY, AL
NASHVILLE, TN
NASHVILLE, TN
NASHVILLE, TN
NASHVILLE, TN
NASHVILLE, TN
NASHVILLE, TN
NEW LONDON, CT
NEW ORLEANS, LA
NEW ORLEANS, LA
NEW ORLEANS, LA
NEW ORLEANS, LA
NEW ORLEANS, LA
NEW ORLEANS, LA
NORFOLK, VA
NORFOLK, VA
OKLAHOMA CITY, OK
OKLAHOMA CITY, OK
OKLAHOMA CITY, OK
OMAHA, NE-IA
ORLANDO, FL
ORLANDO, FL
ORLANDO, FL
OWENSBORO, KY
PARKERSBURG, WV-OH
PARKERSBURG, WV-OH
PENSACOLA, FL
PEORIA-PEKIN, IL
PITTSBURGH, PA
PITTSBURGH, PA
IV-6-7

-------
Pennsylvania
Pennsylvania
Massachusetts
Maine
Maine
New Hampshire
Rhode Island
Rhode Island
Rhode Island
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
Pennsylvania
Virginia
Virginia
Virginia
Virginia
Virginia
New York
New York
Illinois
North Carolina
Florida
Florida
Georgia
Pennsylvania
Pennsylvania
Pennsylvania
Wisconsin
Louisiana
Louisiana
Indiana
Illinois
Massachusetts
Massachusetts
Missouri
Illinois
Illinois
Illinois
Missouri
Missouri
Missouri
Missouri
Pennsylvania
Ohio
Washington
Westmoreland
Berkshire
Cumberland
York
Strafford
Kent
Providence
Washington
Chatham
Durham
Franklin
Johnston
Wake
Berks
Charles City
Chesterfield
Hanover
Henrico
Roanoke
Monroe
Wayne
Winnebago
Edgecombe
Manatee
Sarasota
Chatham
Lackawanna
Luzerne
Mercer
Sheboygan
Bossier
Caddo
St Joseph
Sangamon
Hampden
Hampshire
Greene
Jersey
Madison
St Clair
Jefferson
St Charles
St Louis
St Louis City
Centre
Jefferson
117
125
89
121
126
101
133
117
113
103
103
110
107
114
118
119
114
124
115
94
102
102
93
102
96
99
85
110
110
111
123
98
101
114
98
126
132
101
112
128
108
125
131
119
108
118
111
0.876
0.897
0.880
0.931
0.959
0.917
0.918
0.926
0.909
0.856
0.883
0.868
0.890
0.913
0.876
0.872
0.881
0.882
0.892
0.846
0.923
0.927
0.905
0.908
0.974
0.968
0.924
0.853
0.840
0.885
0.927
0.967
0.967
0.896
0.857
0.906
0.897
0.815
0.896
0.922
0.930
0.915
0.922
0.924
0.927
0.861
0.902
102
112
78
112
120
92
122
108
102
88
90
95
95
104
103
103
100
109
102
79
94
94
84
92
93
95
78
93
92
98
114
94
97
102
84
114
118
82
100
118
100
114
120
109
100
101
100
0.866
0.887
0.869
0.923
0.951
0.911
0.911
0.917
0.900
0.835
0.862
0.846
0.866
0.891
0.864
0.859
0.868
0.866
0.877
0.828
0.913
0.918
0.898
0.893
0.952
0.944
0.905
0.838
0.825
0.868
0.923
0.950
0.950
0.886
0.842
0.898
0.890
0.792
0.875
0.902
0.912
0.896
0.900
0.905
0.911
0.845
0.892
101
110
77
111
119
91
121
107
101
85
88
93
92
101
101
102
98
107
100
77
93
93
83
91
91
93
76
92
90
96
113
93
95
100
82
113
117
80
98
115
98
112
117
107
98
99
98















































PITTSBURGH, PA
PITTSBURGH, PA
PITTSFIELD, MA
PORTLAND, ME
PORTLAND, ME
PORTSMOUTH, NH-ME
PROVIDENCE, RI-MA
PROVIDENCE, RI-MA
PROVIDENCE, RI-MA
RALEIGH, NC
RALEIGH, NC
RALEIGH, NC
RALEIGH, NC
RALEIGH, NC
READING, PA
RICHMOND, VA
RICHMOND, VA
RICHMOND, VA
RICHMOND, VA
ROANOKE, VA
ROCHESTER, NY
ROCHESTER, NY
ROCKFORD, IL
ROCKY MOUNT, NC
SARASOTA, FL
SARASOTA, FL
SAVANNAH, GA
SCRANTON, PA
SCRANTON, PA
SHARON, PA
SHEBOYGAN, Wl
SHREVEPORT, LA
SHREVEPORT, LA
SOUTH BEND, IN
SPRINGFIELD, IL
SPRINGFIELD, MA
SPRINGFIELD, MA
SPRINGFIELD, MO
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
STATE COLLEGE, PA
STEUBENVILLE, OH-WV
IV-6-8

-------
West Virginia
New York
New York
Florida
Florida
Florida
Florida
Indiana
Ohio
Ohio
Oklahoma
Texas
New York
New York
Texas
Wisconsin
Florida
West Virginia
Kansas
Pennsylvania
North Carolina
Pennsylvania
Ohio
Ohio
Alabama
Alabama
Alabama
Arkansas
Arkansas
Delaware
Georgia
Georgia
Georgia
Georgia
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Kentucky
Hancock
Madison
Onondaga
Leon
Hillsborough
Pasco
Pinellas
Vigo
Lucas
Wood
Tulsa
Smith
Herkimer
Oneida
Victoria
Marathon
Palm Beach
Ohio
Sedgwick
Lycoming
New Hanover
York
Ma honing
Trumbull
Clay
Geneva
Sumter
Montgomery
Newton
Sussex
Dawson
Fannin
Glynn
Sumter
Adams
Effing ham
Hamilton
Macoupin
Randolph
Kosciusko
La Porte
Lawrence
Harrison
Palo Alto
Story
Van Buren
Bell
106
89
102
96
112
92
93
107
111
94
121
109
88
95
95
84
89
107
96
91
102
109
109
109
110
84
83
79
83
123
97
92
89
98
89
97
89
102
94
100
146
100
79
70
87
82
92
0.902
0.938
0.911
0.928
0.967
0.965
0.962
0.889
0.915
0.914
0.977
0.955
0.954
0.906
***
0.893
0.979
0.869
0.961
0.880
0.921
0.889
0.891
0.884
0.817
0.893
0.813
***
***
0.880
0.862
0.855
0.920
0.881
0.895
0.838
0.830
0.856
0.841
0.900
0.924
0.823
0.924
***
0.887
0.899
0.795
95
83
92
89
108
88
89
95
101
85
118
104
83
86
***
75
87
92
92
80
93
96
97
96
89
74
67
***
***
108
83
78
81
86
79
81
73
87
79
89
134
82
73
***
77
73
73
0.892
0.925
0.899
0.900
0.953
0.942
0.950
0.878
0.910
0.905
0.964
0.938
0.949
0.892
***
0.883
0.947
0.858
0.948
0.863
0.905
0.875
0.875
0.870
0.797
0.869
0.801
***
***
0.867
0.832
0.823
0.901
0.856
0.887
0.825
0.821
0.837
0.830
0.888
0.919
0.811
0.913
***
0.873
0.890
0.776
94
82
91
86
106
86
88
93
100
85
116
102
83
84
***
74
84
91
91
78
92
95
95
94
87
73
66
***
***
106
80
75
80
83
78
80
73
85
77
88
134
81
72
***
75
72
71















































STEUBENVILLE, OH-WV
SYRACUSE, NY
SYRACUSE, NY
TALLAHASSEE, FL
TAMPA, FL
TAMPA, FL
TAMPA, FL
TERRE HAUTE, IN
TOLEDO, OH
TOLEDO, OH
TULSA, OK
TYLER, TX
UTICA-ROME, NY
UTICA-ROME, NY
VICTORIA, TX
WAUSAU, Wl
WEST PALM BEACH, FL
WHEELING, WV-OH
WICHITA, KS
WILLIAMSPORT, PA
WILMINGTON, NC
YORK, PA
YOUNGSTOWN, OH
YOUNGSTOWN, OH























IV-6-9

-------
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
Edmonson
Graves
Hancock
Hardin
Lawrence
Livingston
McCracken
McLean
Perry
Pike
Pulaski
Simpson
Trigg
Beauregard
Grant
Iberville
Pointe Coupee
St Mary
Hancock
Kennebec
Knox
Oxford
Piscataquis
Sagadahoc
Somerset
Kent
Benzie
Cass
Huron
Mason
Mecosta
Roscommon
Adams
Choctaw
Franklin
Lauderdale
Lee
Sharkey
Warren
Monroe
Ste Genevieve
Belknap
Carroll
Cheshire
Coos
Grafton
Sullivan
113
92
114
113
95
108
100
103
90
98
94
101
106
117
91
139
111
104
121
98
119
79
80
125
92
129
108
115
110
125
110
99
97
81
94
92
96
95
97
96
108
89
88
91
93
77
90
0.792
0.861
0.820
0.827
0.798
0.841
0.855
0.856
0.766
0.782
0.832
0.817
0.805
0.978
0.974
0.990
0.976
0.990
0.916
0.950
0.916
0.911
***
0.933
0.958
0.874
0.933
0.890
0.931
0.919
0.911
0.916
0.968
0.858
0.965
0.848
0.828
0.954
0.977
0.870
0.875
0.814
0.858
0.921
0.847
0.831
0.906
89
79
93
93
75
90
85
88
68
76
78
82
85
114
88
137
108
102
110
93
108
71
***
116
88
112
100
102
102
114
100
90
93
69
90
78
79
90
94
83
94
72
75
83
78
63
81
0.781
0.853
0.811
0.818
0.789
0.833
0.847
0.847
0.754
0.768
0.818
0.805
0.796
0.969
0.961
0.983
0.963
0.984
0.906
0.942
0.906
0.905
***
0.924
0.946
0.862
0.924
0.879
0.923
0.912
0.901
0.907
0.957
0.842
0.954
0.828
0.812
0.945
0.968
0.859
0.860
0.809
0.855
0.910
0.842
0.824
0.897
88
78
92
92
74
89
84
87
67
75
76
81
84
113
87
136
106
102
109
92
107
71
***
115
87
111
99
101
101
113
99
89
92
68
89
76
77
89
93
82
92
71
75
82
78
63
80






























































































IV-6-10

-------
New York
New York
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Vermont
Essex
Hamilton
Jefferson
Ulster
Camden
Caswell
Duplin
Granville
Haywood
Martin
Northampton
Person
Rocking ham
Swain
Yancey
Clinton
Knox
Logan
Preble
Union
Latimer
Okmulgee
Armstrong
Clearfield
Franklin
Greene
Lawrence
Monroe
Abbeville
Barnwell
Chester
Colleton
Darlington
Oconee
Union
Williamsburg
Bradley
Coffee
Dyer
Giles
Hamblen
Haywood
Humphreys
Jefferson
Lawrence
Putnam
Bennington
101
97
110
97
93
111
89
116
103
90
100
100
113
78
108
121
113
100
110
75
100
94
90
116
113
123
101
117
93
99
107
91
94
92
98
85
106
105
112
104
96
97
102
125
93
99
99
0.951
0.813
0.928
0.834
0.920
0.854
0.862
0.875
0.803
0.913
0.846
0.871
0.860
0.790
0.867
0.846
0.893
0.886
0.867
0.875
0.960
0.975
0.890
0.872
0.835
0.796
0.901
0.881
0.883
0.838
0.878
0.862
0.886
0.838
0.816
0.823
0.771
0.836
0.844
0.752
0.798
0.881
0.777
0.803
0.713
0.822
0.911
96
78
102
80
85
94
76
101
82
82
84
87
97
61
93
102
100
88
95
65
96
91
80
101
94
97
90
103
82
82
93
78
83
77
79
69
81
87
94
78
76
85
79
100
66
81
90
0.945
0.804
0.922
0.825
0.910
0.835
0.843
0.854
0.784
0.898
0.832
0.853
0.837
0.771
0.845
0.833
0.881
0.872
0.852
0.862
0.949
0.961
0.879
0.860
0.820
0.785
0.885
0.873
0.862
0.819
0.856
0.843
0.861
0.809
0.797
0.808
0.751
0.817
0.833
0.737
0.784
0.869
0.769
0.785
0.699
0.804
0.901
95
78
101
80
84
92
75
99
80
80
83
85
94
60
91
100
99
87
93
64
94
90
79
99
92
96
89
102
80
81
91
76
80
74
78
68
79
85
93
76
75
84
78
98
65
79
89






























































































IV-6-11

-------
Virginia
Virginia
Virginia
Virginia
Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Caroline
Frederick
Henry
Madison
Wythe
Greenbrier
Columbia
Dodge
Door
Florence
Fond Du Lac
Jefferson
Kewaunee
Manitowoc
Oneida
Polk
Sauk
Vernon
Walworth
109
102
101
99
94
99
104
93
127
80
96
94
121
145
78
85
90
85
100
0.873
0.824
0.813
0.790
0.774
0.731
0.918
0.877
0.945
***
0.886
0.893
0.920
0.919
***
0.929
0.883
0.920
0.894
95
84
82
78
72
72
95
81
120
***
85
83
111
133
***
78
79
78
89
0.858
0.811
0.796
0.777
0.760
0.719
0.906
0.869
0.936
***
0.878
0.884
0.911
0.915
***
0.915
0.872
0.909
0.888
93
82
80
76
71
71
94
80
118
***
84
83
110
132
***
77
78
77
88






































IV-6-12

-------
1996-1998 Design Values
State
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Wisconsin
Kentucky
Kentucky
Kentucky
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Texas
Texas
Texas
Texas
Texas
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Texas
Texas
Texas
Wisconsin
Wisconsin
County
Cook
Du Page
Kane
Lake
McHenry
Will
Lake
Porter
Kenosha
Boone
Campbell
Kenton
Butler
Clermont
Hamilton
Warren
Ashtabula
Cuyahoga
Geauga
Lake
Lorain
Medina
Portage
Summit
Collin
Dallas
Denton
Ellis
Tarrant
Genesee
Lenawee
Macomb
Oakland
St Clair
Washtenaw
Wayne
Brazoria
Galveston
Harris
Milwaukee
Ozaukee
1996-98
Des
Value
125
97
92
124
94
95
113
124
136
108
115
120
118
116
124
124
108
106
116
123
101
105
110
112
128
135
135
130
128
104
99
123
100
118
99
114
134
170
196
129
129
2007-B
RRF
0.920
0.928
0.919
0.921
0.913
0.891
0.912
0.912
0.921
0.839
0.895
0.885
0.876
0.880
0.910
0.895
0.907
0.901
0.894
0.915
0.915
0.904
0.906
0.916
0.941
0.947
0.940
0.967
0.945
0.913
0.915
0.914
0.913
0.913
0.924
0.923
0.975
0.971
0.946
0.918
0.919
2007-B
New
DV
114
90
84
114
85
84
103
113
125
90
102
106
103
102
112
110
97
95
103
112
92
94
99
102
120
127
126
125
120
94
90
112
91
107
91
105
130
165
185
118
118
2007-C
RRF
0.921
0.927
0.925
0.922
0.917
0.883
0.906
0.906
0.922
0.830
0.889
0.878
0.865
0.872
0.905
0.885
0.896
0.892
0.883
0.906
0.910
0.892
0.893
0.906
0.930
0.937
0.925
0.946
0.932
0.901
0.907
0.918
0.922
0.908
0.917
0.925
0.968
0.964
0.946
0.914
0.915
2007-C
New
DV
115
89
85
114
86
83
102
112
125
89
102
105
102
101
112
109
96
94
102
111
91
93
98
101
119
126
124
122
119
93
89
112
92
107
90
105
129
163
185
117
118
CMSA Name
(if applicable)
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Dallas
Dallas
Dallas
Dallas
Dallas
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Houston
Houston
Houston
Milwaukee
Milwaukee
MSA Name
(if applicable)
CHICAGO, IL
CHICAGO, IL
CHICAGO, IL
CHICAGO, IL
CHICAGO, IL
CHICAGO, IL
GARY, IN
GARY, IN
KENOSHA, Wl
CINCINNATI, OH-KY-IN
CINCINNATI, OH-KY-IN
CINCINNATI, OH-KY-IN
HAMILTON, OH
CINCINNATI, OH-KY-IN
CINCINNATI, OH-KY-IN
CINCINNATI, OH-KY-IN
CLEVELAND
CLEVELAND
CLEVELAND
CLEVELAND
CLEVELAND
CLEVELAND
AKRON, OH
AKRON, OH
DALLAS, TX
DALLAS, TX
DALLAS, TX
DALLAS, TX
FORT WORTH, TX
FLINT, Ml
ANN ARBOR, Ml
DETROIT, Ml
DETROIT, Ml
DETROIT, Ml
ANN ARBOR, Ml
DETROIT, Ml
BRAZORIA, TX
GALVESTON, TX
HOUSTON, TX
MILWAUKEE, Wl
MILWAUKEE, Wl
                                                      IV-6-13

-------
Wisconsin
Wisconsin
Wisconsin
Connecticut
Connecticut
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Delaware
Maryland
New Jersey
New Jersey
New Jersey
New Jersey
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
D.C.
Maryland
Maryland
Maryland
Maryland
Maryland
Racine
Washington
Waukesha
Fairfield
New Haven
Bergen
Essex
Hudson
Hunterdon
Mercer
Middlesex
Monmouth
Morris
Ocean
Passaic
Union
Bronx
Dutchess
Kings
New York
Orange
Putnam
Queens
Richmond
Suffolk
Westchester
New Castle
Cecil
Atlantic
Camden
Cumberland
Gloucester
Bucks
Delaware
Montgomery
Philadelphia
Washington
Anne Arundel
Baltimore
Baltimore City
Calvert
Carroll
129
106
103
134
139
116
112
120
119
121
132
129
116
139
120
109
122
111
114
121
115
121
141
138
137
115
127
152
124
129
115
122
119
126
126
125
118
138
126
137
112
115
0.921
0.895
0.900
0.937
0.935
0.954
0.938
0.938
0.921
0.917
0.921
0.934
0.898
0.908
0.918
0.924
0.954
0.799
0.926
0.926
0.860
0.893
0.924
0.934
0.923
0.941
0.849
0.842
0.909
0.912
0.869
0.885
0.910
0.882
0.890
0.903
0.906
0.892
0.906
0.899
0.848
0.882
118
94
92
125
129
110
105
112
109
110
121
120
104
126
110
100
116
88
105
112
98
108
130
128
126
108
107
127
112
117
99
107
108
111
112
112
106
123
114
123
94
101
0.921
0.892
0.897
0.934
0.930
0.954
0.934
0.934
0.913
0.911
0.916
0.927
0.889
0.899
0.913
0.920
0.954
0.794
0.921
0.921
0.858
0.891
0.917
0.932
0.918
0.938
0.838
0.831
0.899
0.902
0.859
0.877
0.904
0.874
0.886
0.899
0.902
0.883
0.899
0.892
0.835
0.871
118
94
92
125
129
110
104
112
108
110
120
119
103
124
109
100
116
88
104
111
98
107
129
128
125
107
106
126
111
116
98
107
107
110
111
112
106
121
113
122
93
100
Milwaukee
Milwaukee
Milwaukee
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
New York City
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
RACINE, Wl
MILWAUKEE, Wl
MILWAUKEE, Wl
NEW HAVEN, CT
NEW HAVEN, CT
BERGEN, NJ
NEWARK, NJ
JERSEY CITY, NJ
MIDDLESEX, NJ
TRENTON, NJ
MIDDLESEX, NJ
MONMOUTH, NJ
NEWARK, NJ
MONMOUTH, NJ
BERGEN, NJ
NEWARK, NJ
NEW YORK, NY
DUTCHESS CO, NY
NEW YORK, NY
NEW YORK, NY
NEWBURGH, NY-PA
NEW YORK, NY
NEW YORK, NY
NEW YORK, NY
NASSAU, NY
NEW YORK, NY
WILMINGTON, DE-MD
WILMINGTON, DE-MD
Atlantic City, NJ
PHILADELPHIA, PA-NJ
VINELAND, NJ
PHILADELPHIA, PA-NJ
PHILADELPHIA, PA-NJ
PHILADELPHIA, PA-NJ
PHILADELPHIA, PA-NJ
PHILADELPHIA, PA-NJ
WASHINGTON, DC
BALTIMORE.MD
BALTIMORE.MD
BALTIMORE.MD
WASHINGTON, DC
BALTIMORE.MD
IV-6-14

-------

Maryland
Maryland
Maryland
Maryland
Maryland
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
New York
New York
New York
Pennsylvania
Pennsylvania
Pennsylvania
Wisconsin
Wisconsin
North Carolina
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
South Carolina
South Carolina
Texas
Maine
Massachusetts

Charles
Frederick
Harford
Montgomery
Prince Georges
Alexandria City
Arlington
Fairfax
Fauquier
Loudoun
Prince William
Stafford
Albany
Saratoga
Schenectady
Lehigh
Northampton
Blair
Outagamie
Winnebago
Buncombe
De Kalb
Douglas
Fayette
Fulton
Gwinnett
Paulding
Rockdale
Richmond
Aiken
Edgefield
Travis
Penobscot
Barnstable

123
108
141
117
129
119
119
125
107
116
115
112
105
99
90
114
111
114
94
94
108
133
133
141
146
134
124
134
118
109
111
110
94
124

0.857
0.880
0.881
0.892
0.892
0.909
0.909
0.904
0.871
0.888
0.877
0.879
0.833
0.846
0.815
0.884
0.893
0.851
0.864
0.902
0.825
0.905
0.896
0.883
0.899
0.894
0.849
0.875
0.886
0.861
0.851
0.936
0.942
0.911

105
95
124
104
115
108
108
112
93
102
100
98
87
83
73
100
99
97
81
84
89
120
119
124
131
119
105
117
104
93
94
102
88
112

0.844
0.869
0.871
0.884
0.883
0.906
0.906
0.896
0.860
0.880
0.867
0.867
0.819
0.829
0.808
0.877
0.883
0.839
0.854
0.892
0.803
0.886
0.866
0.850
0.876
0.860
0.826
0.839
0.857
0.841
0.827
0.919
0.932
0.900

103
93
122
103
113
107
107
112
91
102
99
97
86
82
72
99
97
95
80
83
86
117
115
119
127
115
102
112
101
91
91
101
87
111
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore
Washington-
Baltimore























WASHINGTON, DC
WASHINGTON, DC
BALTIMORE.MD
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
WASHINGTON, DC
ALBANY, NY
ALBANY, NY
ALBANY, NY
ALLENTOWN, PA
ALLENTOWN, PA
ALTOONA, PA
APPLETON, Wl
APPLETON, Wl
ASHEVILLE, NC
ATLANTA, GA
ATLANTA, GA
ATLANTA, GA
ATLANTA, GA
ATLANTA, GA
ATLANTA, GA
ATLANTA, GA
AUGUSTA, GA-SC
AUGUSTA, GA-SC
AUGUSTA, GA-SC
AUSTIN, TX
BANGOR, ME
BARNSTABLE, MA
IV-6-15

-------
Louisiana
Louisiana
Louisiana
Louisiana
Texas
Texas
Michigan
Mississippi
Mississippi
Alabama
Alabama
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
New Hampshire
New York
New York
Vermont
Ohio
Iowa
Illinois
West Virginia
South Carolina
South Carolina
North Carolina
North Carolina
North Carolina
South Carolina
Tennessee
Kentucky
South Carolina
Georgia
Ohio
Ohio
Ohio
Ohio
Texas
Illinois
Iowa
Florida
Ohio
Ohio
Ohio
Ascension
East Baton Rouge
Livingston
West Baton Rouge
Jefferson
Orange
Berrien
Hancock
Jackson
Jefferson
Shelby
Bristol
Essex
Middlesex
Plymouth
Suffolk
Worcester
Rocking ham
Erie
Niagara
Chittenden
Stark
Linn
Champaign
Kanawha
Berkeley
Charleston
Lincoln
Mecklenburg
Rowan
York
Hamilton
Christian
Richland
Muscogee
Delaware
Franklin
Licking
Madison
Nueces
Rock Island
Scott
Volusia
Clark
Greene
Miami
123
126
127
119
130
122
125
105
115
126
128
118
113
113
102
95
115
120
107
101
82
108
78
100
111
101
99
105
131
126
108
125
101
113
103
116
109
112
112
102
83
94
94
119
116
110
0.990
0.981
0.988
0.979
0.984
0.987
0.907
0.985
0.998
0.893
0.882
0.883
0.954
0.923
0.909
0.912
0.910
0.959
0.922
0.920
***
0.904
0.934
0.869
0.891
0.900
0.873
0.872
0.897
0.818
0.874
0.862
0.751
0.896
0.900
0.882
0.897
0.878
0.872
***
0.924
0.921
0.955
0.877
0.865
0.875
121
123
125
116
127
120
113
103
114
112
112
104
107
104
92
86
104
115
98
92
***
97
72
86
98
90
86
91
117
103
94
107
75
101
92
102
97
98
97
***
76
86
89
104
100
96
0.982
0.970
0.981
0.965
0.974
0.979
0.897
0.975
0.988
0.871
0.860
0.872
0.945
0.914
0.897
0.902
0.900
0.951
0.916
0.911
***
0.890
0.925
0.857
0.882
0.876
0.852
0.851
0.877
0.795
0.852
0.829
0.742
0.864
0.867
0.869
0.894
0.864
0.861
***
0.920
0.915
0.928
0.863
0.852
0.860
120
122
124
114
126
119
112
102
113
109
110
102
106
103
91
85
103
114
97
91
***
96
72
85
97
88
84
89
114
100
92
103
74
97
89
100
97
96
96
***
76
86
87
102
98
94














































BATON ROUGE, LA
BATON ROUGE, LA
BATON ROUGE, LA
BATON ROUGE, LA
BEAUMONT, TX
BEAUMONT, TX
BENTON HARBOR, Ml
BILOXI, MS
BILOXI, MS
BIRMINGHAM, AL
BIRMINGHAM, AL
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BOSTON, MA-NH
BUFFALO, NY
BUFFALO, NY
BURLINGTON, VT
CANTON, OH
CEDAR RAPIDS, I A
CHAMPAIGN, IL
CHARLESTON, WV
CHARLESTON, SC
CHARLESTON, SC
CHARLOTTE, NC-SC
CHARLOTTE, NC-SC
CHARLOTTE, NC-SC
CHARLOTTE, NC-SC
CHATTANOOGA, TN-GA
CLARKSVILLE, TN-KY
COLUMBIA, SC
COLUMBUS, GA-AL
COLUMBUS, OH
COLUMBUS, OH
COLUMBUS, OH
COLUMBUS, OH
CORPUS CHRISTI, TX
DAVENPORT, IA-IL
DAVENPORT, IA-IL
DAYTONA BEACH, FL
DAYTON, OH
DAYTON, OH
DAYTON, OH
IV-6-16

-------
Ohio
Alabama
Alabama
Illinois
Iowa
Iowa
Delaware
Indiana
New York
Pennsylvania
Indiana
Indiana
Indiana
Kentucky
North Carolina
Florida
Florida
Indiana
Indiana
Florida
Michigan
Michigan
Michigan
Michigan
Wisconsin
North Carolina
North Carolina
North Carolina
North Carolina
South Carolina
South Carolina
South Carolina
South Carolina
Pennsylvania
Pennsylvania
Connecticut
Connecticut
Connecticut
Connecticut
North Carolina
North Carolina
Louisiana
Kentucky
Kentucky
Kentucky
Ohio
West Virginia
Montgomery
Lawrence
Morgan
Macon
Polk
Warren
Kent
Elkhart
Chemung
Erie
Posey
Vanderburgh
Warrick
Henderson
Cumberland
Lee
St Lucie
Allen
De Kalb
Alachua
Allegan
Kent
Muskegon
Ottawa
Brown
Da vie
Forsyth
Guilford
Pitt
Anderson
Cherokee
Pickens
Spartanburg
Dauphin
Perry
Hartford
Litchfield
Middlesex
Tolland
Alexander
Caldwell
Lafourche
Boyd
Carter
Greenup
Lawrence
Cabell
112
101
114
100
76
79
128
113
93
117
105
114
115
108
108
98
88
101
82
105
123
106
121
106
105
113
120
112
109
118
116
109
112
112
103
139
120
135
132
110
111
110
107
118
118
123
129
0.874
0.813
0.876
0.859
0.889
0.881
0.901
0.884
0.900
0.908
0.875
0.873
0.853
0.865
0.905
0.989
0.997
0.904
0.904
0.923
0.917
0.897
0.919
0.919
0.898
0.809
0.862
0.864
0.900
0.870
0.860
0.847
0.853
0.887
0.844
0.923
0.897
0.939
0.919
0.822
0.869
0.987
0.858
0.824
0.842
0.837
0.862
97
82
99
85
67
69
115
99
83
106
91
99
98
93
97
96
87
91
74
96
112
95
111
97
94
91
103
96
98
102
99
92
95
99
86
128
107
126
121
90
96
108
91
97
99
102
111
0.864
0.797
0.861
0.846
0.874
0.867
0.887
0.873
0.884
0.897
0.865
0.862
0.844
0.856
0.878
0.966
0.975
0.890
0.890
0.900
0.911
0.887
0.912
0.913
0.889
0.790
0.836
0.840
0.882
0.846
0.840
0.823
0.829
0.866
0.825
0.917
0.888
0.933
0.910
0.807
0.847
0.980
0.847
0.814
0.832
0.826
0.851
96
80
98
84
66
68
113
98
82
105
90
98
97
92
94
94
85
89
72
94
111
94
110
96
93
89
100
94
96
99
97
89
92
97
85
127
106
126
120
88
93
107
90
96
98
101
109















































DAYTON, OH
DECATUR, AL
DECATUR, AL
DECATUR, IL
DES MOINES, IA
DES MOINES, IA
DOVER, DE
ELKHART, IN
ELMIRA, NY
ERIE, PA
EVANSVILLE, IN-KY
EVANSVILLE, IN-KY
EVANSVILLE, IN-KY
EVANSVILLE, IN-KY
FAYETTEVILLE, NC
FORT MEYERS, FL
FORT PIERCE, FL
FORT WAYNE, IN
FORT WAYNE, IN
GAINESVILLE, FL
GRAND RAPIDS-MUSKEGON, Ml
GRAND RAPIDS-MUSKEGON, Ml
GRAND RAPIDS-MUSKEGON, Ml
GRAND RAPIDS-MUSKEGON, Ml
GREEN BAY, Wl
GREENSBORO, NC
GREENSBORO, NC
GREENSBORO, NC
GREENVILLE, NC
GREENVILLE, SC
GREENVILLE, SC
GREENVILLE, SC
GREENVILLE, SC
HARRISBURG, PA
HARRISBURG, PA
HARTFORD, CT
HARTFORD, CT
HARTFORD, CT
HARTFORD, CT
HICKORY, NC
HICKORY NC
HOUMA, LA
HUNTINGTON, WV-KY-OH
HUNTINGTON, WV-KY-OH
HUNTINGTON, WV-KY-OH
HUNTINGTON, WV-KY-OH
HUNTINGTON, WV-KY-OH
IV-6-17

-------
Alabama
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Mississippi
Mississippi
Florida
Florida
New York
Wisconsin
Tennessee
Pennsylvania
Michigan
Kansas
Kansas
Missouri
Missouri
Missouri
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Louisiana
Louisiana
Florida
Pennsylvania
Michigan
Michigan
Kentucky
Kentucky
Kentucky
Ohio
Nebraska
Arkansas
Texas
Indiana
Indiana
Kentucky
Kentucky
Kentucky
New Hampshire
Georgia
Wisconsin
Madison
Hamilton
Hancock
Johnson
Madison
Marion
Morgan
Hinds
Madison
Duval
St Johns
Chautauqua
Rock
Sullivan
Cambria
Kalamazoo
Miami
Wyandotte
Clay
Jackson
Platte
Anderson
Blount
Knox
Loudon
Sevier
Lafayette
Calcasieu
Polk
Lancaster
Clinton
Ingham
Fayette
Jessamine
Scott
Allen
Lancaster
Pulaski
Gregg
Clark
Floyd
Bullitt
Jefferson
Oldham
Hillsborough
Bibb
Dane
104
125
120
102
112
118
103
104
101
111
91
106
100
113
112
105
114
113
124
94
122
107
118
134
112
119
101
122
102
121
93
97
101
102
103
102
64
102
128
130
127
111
121
120
110
134
94
0.899
0.900
0.902
0.863
0.901
0.904
0.887
0.944
0.968
0.965
0.953
0.910
0.898
0.861
0.875
0.897
0.944
0.950
0.947
0.933
0.946
0.834
0.840
0.860
0.846
0.796
0.974
0.987
0.970
0.895
0.914
0.916
0.885
0.878
0.831
0.892
0.938
0.952
0.977
0.889
0.895
0.874
0.892
0.871
0.902
0.808
0.921
93
112
108
88
100
106
91
98
97
107
86
96
89
97
98
94
107
107
117
87
115
89
99
115
94
94
98
120
98
108
84
88
89
89
85
91
60
97
125
115
113
96
107
104
99
108
86
0.878
0.889
0.890
0.853
0.888
0.896
0.881
0.921
0.960
0.936
0.925
0.899
0.890
0.847
0.864
0.886
0.933
0.941
0.935
0.923
0.937
0.813
0.816
0.838
0.824
0.778
0.964
0.979
0.946
0.882
0.904
0.907
0.872
0.866
0.819
0.880
0.925
0.927
0.960
0.883
0.891
0.868
0.888
0.864
0.895
0.783
0.909
91
111
106
87
99
105
90
95
96
103
84
95
88
95
96
93
106
106
115
86
114
86
96
112
92
92
97
119
96
106
84
87
88
88
84
89
59
94
122
114
113
96
107
103
98
104
85















































HUNTSVILLE, AL
INDIANAPOLIS, IN
INDIANAPOLIS, IN
INDIANAPOLIS, IN
INDIANAPOLIS, IN
INDIANAPOLIS, IN
INDIANAPOLIS, IN
JACKSON, MS
JACKSON, MS
JACKSONVILLE, FL
JACKSONVILLE, FL
JAMESTOWN, NY
JANESVILLE-BELOIT, Wl
JOHNSON CITY, TN-VA
JOHNSTOWN, PA
KALAMAZOO, Ml
KANSAS CITY, MO-KS
KANSAS CITY, MO-KS
KANSAS CITY, MO-KS
KANSAS CITY, MO-KS
KANSAS CITY, MO-KS
KNOXVILLE, TN
KNOXVILLE, TN
KNOXVILLE, TN
KNOXVILLE, TN
KNOXVILLE, TN
LAFAYETTE, LA
LAKE CHARLES, LA
LAKELAND, FL
LANCASTER, PA
LANSING, Ml
LANSING, Ml
LEXINGTON, KY
LEXINGTON, KY
LEXINGTON, KY
LIMA, OH
LINCOLN, NE
LITTLE ROCK, AR
LONGVIEW, TX
LOUISVILLE, KY-IN
LOUISVILLE, KY-IN
LOUISVILLE, KY-IN
LOUISVILLE, KY-IN
LOUISVILLE, KY-IN
LOWELL, MA-NH
MACON, GA
MADISON, Wl
IV-6-18

-------
New Hampshire
Florida
Arkansas
Mississippi
Tennessee
Minnesota
Minnesota
Minnesota
Wisconsin
Alabama
Louisiana
Alabama
Alabama
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Connecticut
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Virginia
Virginia
Florida
Oklahoma
Oklahoma
Oklahoma
Nebraska
Florida
Florida
Florida
Kentucky
Ohio
West Virginia
Florida
Illinois
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Massachusetts
Maine
Maine
Merrimack
Brevard
Crittenden
De Soto
Shelby
Anoka
Dakota
Washington
St Croix
Mobile
Ouachita
Elmore
Montgomery
Davidson
Rutherford
Sumner
Williamson
Wilson
New London
Jefferson
Orleans
St Bernard
St Charles
St James
St John The Baptis
Hampton City
Suffolk City
Marion
Cleveland
Me Clain
Oklahoma
Douglas
Orange
Osceola
Seminole
Daviess
Washington
Wood
Escambia
Peoria
Allegheny
Beaver
Washington
Westmoreland
Berkshire
Cumberland
York
98
93
118
131
123
93
87
97
88
114
94
109
118
120
101
127
114
108
137
111
92
105
108
108
109
109
108
101
104
98
109
82
109
104
100
108
110
111
117
89
122
113
123
104
108
121
121
0.864
0.986
0.914
0.941
0.943
0.913
0.918
0.948
0.938
0.920
0.976
0.894
0.891
0.901
0.870
0.887
0.813
0.858
0.930
0.970
0.969
0.982
0.978
0.990
0.982
0.912
0.908
0.936
0.972
0.993
0.979
0.928
0.985
0.983
0.975
0.846
0.843
0.830
0.967
0.899
0.895
0.932
0.876
0.897
0.880
0.932
0.932
84
91
107
123
116
84
79
91
82
104
91
97
105
108
87
112
92
92
127
107
89
103
105
106
107
99
98
94
101
97
106
76
107
102
97
91
92
92
113
79
109
105
107
93
95
112
112
0.858
0.960
0.904
0.928
0.935
0.912
0.915
0.938
0.924
0.904
0.965
0.871
0.867
0.882
0.846
0.869
0.795
0.841
0.924
0.962
0.963
0.975
0.972
0.982
0.975
0.904
0.900
0.910
0.952
0.972
0.962
0.917
0.956
0.955
0.945
0.838
0.833
0.821
0.949
0.890
0.885
0.921
0.866
0.887
0.869
0.923
0.923
84
89
106
121
114
84
79
91
81
103
90
94
102
105
85
110
90
90
126
106
88
102
104
106
106
98
97
91
98
95
104
75
104
99
94
90
91
91
111
79
107
104
106
92
93
111
111















































MANCHESTER, NH
MELBOURNE, FL
MEMPHIS, TN-AR-MS
MEMPHIS, TN-AR-MS
MEMPHIS, TN-AR-MS
MINNEAPOLIS, MN-WI
MINNEAPOLIS, MN-WI
MINNEAPOLIS, MN-WI
MINNEAPOLIS, MN-WI
MOBILE, AL
MONROE, LA
MONTGOMERY, AL
MONTGOMERY, AL
NASHVILLE, TN
NASHVILLE, TN
NASHVILLE, TN
NASHVILLE, TN
NASHVILLE, TN
NEW LONDON, CT-MA
NEW ORLEANS, LA
NEW ORLEANS, LA
NEW ORLEANS, LA
NEW ORLEANS, LA
NEW ORLEANS, LA
NEW ORLEANS, LA
NORFOLK, VA
NORFOLK, VA
OCALA, FL
OKLAHOMA CITY, OK
OKLAHOMA CITY, OK
OKLAHOMA CITY, OK
OMAHA, NE-IA
ORLANDO, FL
ORLANDO, FL
ORLANDO, FL
OWENSBORO, KY
PARKERSBURG, WV-OH
PARKERSBURG, WV-OH
PENSACOLA, FL
PEORIA-PEKIN, IL
PITTSBURGH, PA
PITTSBURGH, PA
PITTSBURGH, PA
PITTSBURGH, PA
PITTSFIELD, MA
PORTLAND, ME
PORTLAND, ME
IV-6-19

-------
New Hampshire
Rhode Island
Rhode Island
Rhode Island
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
Pennsylvania
Virginia
Virginia
Virginia
Virginia
Virginia
New York
New York
Illinois
North Carolina
Florida
Florida
Georgia
Pennsylvania
Pennsylvania
Pennsylvania
Wisconsin
Louisiana
Louisiana
Indiana
Illinois
Massachusetts
Massachusetts
Missouri
Illinois
Illinois
Illinois
Missouri
Missouri
Missouri
Missouri
Pennsylvania
Ohio
West Virginia
New York
New York
Florida
Florida
Strafford
Kent
Providence
Washington
Chatham
Durham
Franklin
Johnston
Wake
Berks
Charles City
Chesterfield
Hanover
Henri co
Roanoke
Monroe
Wayne
Winnebago
Edgecombe
Manatee
Sarasota
Chatham
Lackawanna
Luzerne
Mercer
Sheboygan
Bossier
Caddo
St Joseph
Sangamon
Hampden
Hampshire
Greene
Jersey
Madison
St Clair
Jefferson
St Charles
St Louis
St Louis City
Centre
Jefferson
Hancock
Madison
Onondaga
Leon
Hillsborough
101
114
108
111
106
110
112
110
116
117
123
114
125
116
110
96
101
86
106
102
106
87
108
110
117
138
109
102
117
96
116
128
94
122
118
98
118
131
119
107
113
94
99
90
95
97
123
0.917
0.918
0.926
0.909
0.856
0.883
0.868
0.890
0.908
0.876
0.872
0.881
0.882
0.892
0.846
0.923
0.927
0.905
0.908
0.974
0.968
0.924
0.853
0.840
0.885
0.927
0.967
0.967
0.896
0.857
0.906
0.897
0.815
0.896
0.922
0.930
0.915
0.922
0.924
0.927
0.861
0.903
0.903
0.938
0.911
0.928
0.973
92
104
100
100
90
97
97
97
105
102
107
100
110
103
93
88
93
77
96
99
102
80
92
92
103
127
105
98
104
82
105
114
76
109
108
91
107
120
109
99
97
84
89
84
86
90
119
0.911
0.911
0.917
0.900
0.835
0.862
0.846
0.866
0.885
0.864
0.859
0.868
0.866
0.877
0.828
0.913
0.918
0.898
0.893
0.952
0.944
0.905
0.838
0.825
0.868
0.923
0.950
0.950
0.886
0.842
0.898
0.890
0.792
0.875
0.902
0.912
0.896
0.900
0.905
0.911
0.845
0.892
0.892
0.925
0.899
0.900
0.960
91
103
99
99
88
94
94
95
102
101
105
98
108
101
91
87
92
77
94
97
100
78
90
90
101
127
103
96
103
80
104
113
74
106
106
89
105
117
107
97
95
83
88
83
85
87
118















































PORTSMOUTH, NH-ME
PROVIDENCE, RI-MA
PROVIDENCE, RI-MA
PROVIDENCE, RI-MA
RALEIGH, NC
RALEIGH, NC
RALEIGH, NC
RALEIGH, NC
RALEIGH, NC
READING, PA
RICHMOND, VA
RICHMOND, VA
RICHMOND, VA
RICHMOND, VA
ROANOKE, VA
ROCHESTER, NY
ROCHESTER, NY
ROCKFORD, IL
ROCKY MOUNT, NC
SARASOTA, FL
SARASOTA, FL
SAVANNAH, GA
SCRANTON, PA
SCRANTON-, PA
SHARON, PA
SHEBOYGAN, Wl
SHREVEPORT, LA
SHREVEPORT, LA
SOUTH BEND, IN
SPRINGFIELD, IL
SPRINGFIELD, MA
SPRINGFIELD, MA
SPRINGFIELD, MO
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
ST. LOUIS, MO-IL
STATE COLLEGE, PA
STEUBENVILLE, OH-WV
STEUBENVILLE, OH-WV
SYRACUSE, NY
SYRACUSE, NY
TALLAHASSEE, FL
TAMPA, FL
IV-6-20

-------
Florida
Florida
Indiana
Ohio
Ohio
Oklahoma
Texas
New York
New York
Texas
Wisconsin
Florida
West Virginia
Kansas
Pennsylvania
North Carolina
Pennsylvania
Ohio
Ohio
Alabama
Alabama
Alabama
Arkansas
Arkansas
Delaware
Georgia
Georgia
Georgia
Georgia
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Pasco
Pinellas
Vigo
Lucas
Wood
Tulsa
Smith
Herkimer
Oneida
Victoria
Marathon
Palm Beach
Ohio
Sedgwick
Lycoming
New Hanover
York
Ma honing
Trumbull
Clay
Geneva
Sumter
Montgomery
Newton
Sussex
Dawson
Fannin
Glynn
Sumter
Adams
Effing ham
Hamilton
Macoupin
Randolph
La Porte
Lawrence
Perry
Harrison
Palo Alto
Story
Van Buren
Linn
Bell
Edmonson
Graves
Hancock
Hardin
98
104
107
108
97
116
107
84
91
92
83
102
105
97
95
102
109
111
114
110
88
81
85
84
125
108
96
99
98
98
96
89
107
97
128
100
114
92
81
87
82
104
98
108
102
111
96
0.965
0.962
0.889
0.920
0.914
0.977
0.955
0.954
0.906
***
0.893
0.979
0.869
0.961
0.875
0.921
0.889
0.891
0.890
0.817
0.893
0.813
***
***
0.880
0.862
0.855
0.920
0.881
0.895
0.838
0.830
0.856
0.841
0.924
0.823
0.820
0.924
***
0.887
0.899
0.961
0.795
0.792
0.861
0.820
0.827
94
100
95
99
88
113
102
80
82
***
74
99
91
93
83
93
96
98
101
89
78
65
***
***
109
93
82
91
86
87
80
73
91
81
118
82
93
85
***
77
73
99
77
85
87
91
79
0.942
0.954
0.878
0.914
0.905
0.965
0.939
0.949
0.892
***
0.883
0.947
0.858
0.948
0.861
0.905
0.875
0.875
0.875
0.797
0.869
0.801
***
***
0.867
0.832
0.823
0.901
0.856
0.887
0.825
0.821
0.837
0.830
0.919
0.811
0.812
0.913
***
0.873
0.890
0.949
0.776
0.781
0.853
0.811
0.818
92
99
93
98
87
111
100
79
81
***
73
96
90
91
81
92
95
97
99
87
76
64
***
***
108
89
78
89
83
86
79
73
89
80
117
81
92
84
***
75
72
98
76
84
86
90
78















































TAMPA, FL
TAMPA, FL
TERRE HAUTE, IN
TOLEDO, OH
TOLEDO, OH
TULSA, OK
TYLER, TX
UTICA-ROME, NY
UTICA-ROME, NY
VICTORIA, TX
WAUSAU, Wl
WEST PALM BEACH, FL
WHEELING, WV-OH
WICHITA, KS
WILLIAMSPORT, PA
WILMINGTON, NC
YORK, PA
YOUNGSTOWN, OH
YOUNGSTOWN, OH




























IV-6-21

-------
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New York
New York
Lawrence
Livingston
McCracken
McLean
Perry
Pike
Pulaski
Simpson
Trigg
Beauregard
Grant
Iberville
Pointe Coupee
St Mary
Hancock
Kennebec
Knox
Oxford
Piscataquis
Sagadahoc
Somerset
Kent
Benzie
Cass
Huron
Mason
Mecosta
Missaukee
Roscommon
Adams
Choctaw
Lauderdale
Lee
Panola
Sharkey
Warren
Cedar
Monroe
Ste Genevieve
Belknap
Carroll
Cheshire
Coos
Grafton
Sullivan
Essex
Hamilton
95
113
109
106
90
100
95
113
106
109
95
126
111
102
118
102
113
77
68
124
93
126
107
115
106
123
124
97
99
97
81
92
107
119
95
97
96
97
106
88
79
91
101
84
93
93
91
0.798
0.841
0.855
0.856
0.766
0.782
0.832
0.817
0.805
0.978
0.974
0.990
0.976
0.990
0.916
0.950
0.916
***
***
0.933
0.958
0.874
0.933
0.890
0.931
0.919
0.911
0.912
0.916
0.968
0.858
0.848
0.828
0.887
0.954
0.977
0.946
0.870
0.875
0.814
0.858
0.921
0.847
0.831
0.906
0.951
0.813
75
95
93
90
68
78
79
92
85
106
92
124
108
100
108
96
103
***
***
115
89
110
99
102
98
113
112
88
90
93
69
78
88
105
90
94
90
84
92
71
67
83
85
69
84
88
74
0.789
0.833
0.847
0.847
0.754
0.768
0.818
0.805
0.796
0.969
0.961
0.983
0.963
0.984
0.906
0.942
0.906
***
***
0.924
0.946
0.862
0.924
0.879
0.923
0.912
0.901
0.902
0.907
0.957
0.842
0.828
0.812
0.873
0.945
0.968
0.937
0.859
0.860
0.809
0.855
0.910
0.842
0.824
0.897
0.945
0.804
74
94
92
89
67
76
77
90
84
105
91
123
106
100
106
96
102
***
***
114
87
108
98
101
97
112
111
87
89
92
68
76
86
103
89
93
89
83
91
71
67
82
85
69
83
87
73






























































































IV-6-22

-------
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Jefferson
Ulster
Avery
Camden
Caswell
Duplin
Granville
Haywood
Lenoir
Martin
Northampton
Person
Rocking ham
Swain
Yancey
Clinton
Knox
Logan
Preble
Union
Latimer
Mayes
Muskogee
Okmulgee
Armstrong
Clearfield
Franklin
Greene
Lawrence
Monroe
Abbeville
Barnwell
Chester
Colleton
Darlington
Oconee
Union
Williamsburg
Bradley
Coffee
Giles
Hamblen
Haywood
Humphreys
Jefferson
Lawrence
Putnam
104
96
96
93
118
99
124
106
109
94
103
117
123
87
94
118
108
99
111
96
108
106
93
104
113
117
115
123
98
116
103
108
113
99
99
103
99
89
106
96
104
96
120
102
126
100
106
0.928
0.834
0.884
0.920
0.854
0.862
0.875
0.827
0.879
0.913
0.846
0.871
0.860
0.790
0.867
0.846
0.893
0.886
0.867
0.875
0.960
0.974
0.987
0.975
0.890
0.872
0.835
0.796
0.901
0.881
0.883
0.838
0.878
0.862
0.886
0.838
0.816
0.823
0.771
0.836
0.752
0.798
0.881
0.777
0.803
0.713
0.822
96
80
84
85
100
85
108
87
95
85
87
101
105
68
81
99
96
87
96
84
103
103
91
101
100
102
95
97
88
102
90
90
99
85
87
86
80
73
81
80
78
76
105
79
101
71
87
0.922
0.825
0.862
0.910
0.835
0.843
0.854
0.804
0.860
0.898
0.832
0.853
0.837
0.771
0.845
0.833
0.881
0.872
0.852
0.862
0.949
0.962
0.972
0.961
0.879
0.860
0.820
0.785
0.885
0.873
0.862
0.819
0.856
0.843
0.861
0.809
0.797
0.808
0.751
0.817
0.737
0.784
0.869
0.769
0.785
0.699
0.804
95
79
82
84
98
83
105
85
93
84
85
99
103
67
79
98
95
86
94
82
102
102
90
99
99
100
94
96
86
101
88
88
96
83
85
83
78
71
79
78
76
75
104
78
98
69
85






























































































IV-6-23

-------
Texas
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Marion
Bennington
Caroline
Frederick
Henry
Madison
Wythe
Greenbrier
Columbia
Dodge
Florence
Fond Du Lac
Jefferson
Kewaunee
Manitowoc
Oneida
Polk
Sauk
Vernon
Walworth
94
98
111
109
101
115
96
111
91
100
83
96
93
117
158
82
81
89
83
100
0.973
0.911
0.873
0.824
0.813
0.790
0.774
0.731
0.918
0.877
***
0.886
0.893
0.920
0.919
***
0.929
0.883
0.920
0.894
91
89
96
89
82
90
74
81
83
87
***
85
83
107
145
***
75
78
76
89
0.955
0.901
0.858
0.811
0.796
0.777
0.760
0.719
0.906
0.869
***
0.878
0.884
0.911
0.915
***
0.915
0.872
0.909
0.888
89
88
95
88
80
89
72
79
82
86
***
84
82
106
144
***
74
77
75
88








































IV-6-24

-------